feat: add comparable localization algorithms

2026-08-28 20:20:57 +09:00
parent 7fe846fbf9
commit d58f673402
20 changed files with 2104 additions and 68 deletions
@@ -5,6 +5,11 @@ import {
  requireMaxCompensationDb,
 } from "./frequency-response.js";
 import { cooperativeYield } from "./cooperative-yield.js";
+import {
+  DEFAULT_PHAT_RELATIVE_FLOOR,
+  isDasAlgorithm,
+  requireLocalizationAlgorithmDefinition,
+} from "./localization-algorithms.js";
 import { binsForBand, createStftWorkspace, extractStftBins, getFrameCount } from "./stft.js";

 function throwIfAborted(signal) {
@@ -48,20 +53,24 @@ function createBandIndexLookup(bandBins) {
  return lookup;
 }

-function createBinCalibration(
+export function createBinCalibration(options) {
+  const {
    arrayDefinition,
    bin,
    sampleRate,
    fftSize,
-  compensateFrequencyResponse,
-  maximumCompensationDb,
-) {
+    compensateFrequencyResponse = true,
+    maxCompensationDb: maximumCompensationDb,
+    includeAmplitude = true,
+  } = options;
  const microphoneCount = arrayDefinition.microphones.length;
  const gains = new Float64Array(microphoneCount);
  const phases = new Float64Array(microphoneCount);
  const frequencyHz = (bin * sampleRate) / fftSize;
  const response = {};
-  const maxCompensationDb = requireMaxCompensationDb(maximumCompensationDb);
+  const maxCompensationDb = includeAmplitude
+    ? requireMaxCompensationDb(maximumCompensationDb)
+    : 0;
  for (let microphoneIndex = 0; microphoneIndex < microphoneCount; microphoneIndex += 1) {
    const microphone = arrayDefinition.microphones[microphoneIndex];
    let responseGainDb = 0;
@@ -71,23 +80,72 @@ function createBinCalibration(
      responseGainDb = response.gainDb;
      responsePhaseDeg = response.phaseDeg;
    }
-    const responseCompensationDb = compensateFrequencyResponse
+    const responseCompensationDb = includeAmplitude && compensateFrequencyResponse
      ? inverseResponseGainDb(responseGainDb, maxCompensationDb)
      : 0;
-    gains[microphoneIndex] = dbToLinear(
-      -(microphone.sensitivityDb ?? 0) + responseCompensationDb,
-    );
+    gains[microphoneIndex] = includeAmplitude
+      ? dbToLinear(-(microphone.sensitivityDb ?? 0) + responseCompensationDb)
+      : 1;
    phases[microphoneIndex] =
      ((microphone.phaseOffsetDeg ?? 0) + responsePhaseDeg) * Math.PI / 180;
  }
  return { gains, phases };
 }

+function requirePhatRelativeFloor(value) {
+  if (!Number.isFinite(value) || value < 0 || value > 1) {
+    throw new RangeError("phatRelativeFloor must lie inside 0..1.");
+  }
+  return value;
+}
+
+export function phaseNormalizeSpectrum(
+  spectrum,
+  frameCount,
+  microphoneCount,
+  relativeFloor = DEFAULT_PHAT_RELATIVE_FLOOR,
+) {
+  const expectedLength = frameCount * microphoneCount;
+  if (
+    !spectrum ||
+    spectrum.real?.length !== expectedLength ||
+    spectrum.imaginary?.length !== expectedLength
+  ) {
+    throw new RangeError("spectrum length must match frameCount and microphoneCount.");
+  }
+  requirePhatRelativeFloor(relativeFloor);
+  const real = new Float32Array(expectedLength);
+  const imaginary = new Float32Array(expectedLength);
+  for (let frameIndex = 0; frameIndex < frameCount; frameIndex += 1) {
+    const frameOffset = frameIndex * microphoneCount;
+    let maximumMagnitude = 0;
+    for (let microphoneIndex = 0; microphoneIndex < microphoneCount; microphoneIndex += 1) {
+      const spectrumIndex = frameOffset + microphoneIndex;
+      maximumMagnitude = Math.max(
+        maximumMagnitude,
+        Math.hypot(spectrum.real[spectrumIndex], spectrum.imaginary[spectrumIndex]),
+      );
+    }
+    const magnitudeFloor = maximumMagnitude * relativeFloor;
+    for (let microphoneIndex = 0; microphoneIndex < microphoneCount; microphoneIndex += 1) {
+      const spectrumIndex = frameOffset + microphoneIndex;
+      const inputReal = spectrum.real[spectrumIndex];
+      const inputImaginary = spectrum.imaginary[spectrumIndex];
+      const magnitude = Math.hypot(inputReal, inputImaginary);
+      if (!(magnitude > magnitudeFloor)) continue;
+      real[spectrumIndex] = inputReal / magnitude;
+      imaginary[spectrumIndex] = inputImaginary / magnitude;
+    }
+  }
+  return { real, imaginary };
+}
+
 function fillSteeringVector({
  angularWaveNumber,
  distances,
  distanceOffset,
  calibration,
+  applyCalibrationGains = true,
  outputReal,
  outputImaginary,
  outputOffset,
@@ -96,10 +154,9 @@ function fillSteeringVector({
    const phase =
      angularWaveNumber * distances[distanceOffset + microphoneIndex] -
      calibration.phases[microphoneIndex];
-    outputReal[outputOffset + microphoneIndex] =
-      Math.cos(phase) * calibration.gains[microphoneIndex];
-    outputImaginary[outputOffset + microphoneIndex] =
-      Math.sin(phase) * calibration.gains[microphoneIndex];
+    const gain = applyCalibrationGains ? calibration.gains[microphoneIndex] : 1;
+    outputReal[outputOffset + microphoneIndex] = Math.cos(phase) * gain;
+    outputImaginary[outputOffset + microphoneIndex] = Math.sin(phase) * gain;
  }
 }

@@ -113,6 +170,7 @@ async function createSteeringMatrix(options) {
    fftSize,
    speedOfSound,
    calibration,
+    applyCalibrationGains = true,
    signal,
    pointBatchSize,
  } = options;
@@ -127,6 +185,7 @@ async function createSteeringMatrix(options) {
      distances,
      distanceOffset: offset,
      calibration,
+      applyCalibrationGains,
      outputReal: real,
      outputImaginary: imaginary,
      outputOffset: offset,
@@ -158,21 +217,32 @@ export async function computeBinMap(options) {
    steeringImaginary: providedSteeringImaginary,
    signal,
    pointBatchSize = 64,
+    algorithm: algorithmId = "das",
+    phatRelativeFloor = DEFAULT_PHAT_RELATIVE_FLOOR,
  } = options;
+  const algorithm = requireLocalizationAlgorithmDefinition(algorithmId);
+  if (!isDasAlgorithm(algorithm.id)) {
+    throw new RangeError(`computeBinMap does not implement ${algorithm.id}.`);
+  }
+  const phaseOnly = algorithm.spectralWeighting === "phat";
  const microphoneCount = arrayDefinition.microphones.length;
  const output = providedOutput ?? new Float32Array(pointCount);
  const steeringReal = providedSteeringReal ?? new Float64Array(microphoneCount);
  const steeringImaginary = providedSteeringImaginary ?? new Float64Array(microphoneCount);
  const angularWaveNumber = (TAU * bin * sampleRate) / (fftSize * speedOfSound);
  const microphoneScale = 1 / microphoneCount;
-  const calibration = providedCalibration ?? createBinCalibration(
+  const calibration = providedCalibration ?? createBinCalibration({
    arrayDefinition,
    bin,
    sampleRate,
    fftSize,
    compensateFrequencyResponse,
    maxCompensationDb,
-  );
+    includeAmplitude: !phaseOnly,
+  });
+  const inputSpectrum = phaseOnly
+    ? phaseNormalizeSpectrum(spectrum, frameCount, microphoneCount, phatRelativeFloor)
+    : spectrum;
  const phaseRealValues = steeringMatrix?.real ?? steeringReal;
  const phaseImaginaryValues = steeringMatrix?.imaginary ?? steeringImaginary;
  if (output.length !== pointCount) throw new RangeError("output length must match pointCount.");
@@ -208,6 +278,7 @@ export async function computeBinMap(options) {
        outputReal: steeringReal,
        outputImaginary: steeringImaginary,
        outputOffset: 0,
+        applyCalibrationGains: !phaseOnly,
      });
    }

@@ -218,8 +289,8 @@ export async function computeBinMap(options) {
      let sumImaginary = 0;
      for (let microphoneIndex = 0; microphoneIndex < microphoneCount; microphoneIndex += 1) {
        const spectrumIndex = spectrumOffset + microphoneIndex;
-        const inputReal = spectrum.real[spectrumIndex];
-        const inputImaginary = spectrum.imaginary[spectrumIndex];
+        const inputReal = inputSpectrum.real[spectrumIndex];
+        const inputImaginary = inputSpectrum.imaginary[spectrumIndex];
        const phaseReal = phaseRealValues[steeringOffset + microphoneIndex];
        const phaseImaginary = phaseImaginaryValues[steeringOffset + microphoneIndex];
        sumReal += inputReal * phaseReal - inputImaginary * phaseImaginary;
@@ -265,6 +336,12 @@ export function createDasAnalysisPlan(options) {
  if (!Number.isSafeInteger(maximumPlanBytes) || maximumPlanBytes <= 0) {
    throw new RangeError("maximumPlanBytes must be a positive safe integer.");
  }
+  const algorithm = requireLocalizationAlgorithmDefinition(
+    experiment.beamforming?.algorithm ?? "das",
+  );
+  if (!isDasAlgorithm(algorithm.id)) {
+    throw new RangeError(`createDasAnalysisPlan does not implement ${algorithm.id}.`);
+  }

  const { fftSize } = experiment.stft;
  const bandBins = experiment.frequencyBands.map((band) => binsForBand(band, sampleRate, fftSize));
@@ -319,6 +396,7 @@ export function createDasAnalysisPlan(options) {
    arrayDefinition,
    scene,
    experiment,
+    algorithm,
    microphoneCount,
    pointCount,
    bandBins,
@@ -394,14 +472,16 @@ function planCalibration(plan, bin) {
    plan.statistics.calibrationCacheHits += 1;
    return calibration;
  }
-  calibration = createBinCalibration(
-    plan.arrayDefinition,
+  calibration = createBinCalibration({
+    arrayDefinition: plan.arrayDefinition,
    bin,
-    plan.sampleRate,
-    plan.experiment.stft.fftSize,
+    sampleRate: plan.sampleRate,
+    fftSize: plan.experiment.stft.fftSize,
+    compensateFrequencyResponse:
      plan.experiment.beamforming.compensateFrequencyResponse !== false,
-    plan.experiment.beamforming.maxCompensationDb,
-  );
+    maxCompensationDb: plan.experiment.beamforming.maxCompensationDb,
+    includeAmplitude: plan.algorithm.spectralWeighting !== "phat",
+  });
  plan.calibrations.set(bin, calibration);
  plan.statistics.calibrationBuilds += 1;
  plan.statistics.retainedBytes += calibration.gains.byteLength + calibration.phases.byteLength;
@@ -431,6 +511,7 @@ async function planSteeringMatrix(plan, bin, calibration, signal, pointBatchSize
    fftSize: plan.experiment.stft.fftSize,
    speedOfSound: plan.scene.speedOfSound,
    calibration,
+    applyCalibrationGains: plan.algorithm.spectralWeighting !== "phat",
    signal,
    pointBatchSize,
  });
@@ -520,7 +601,10 @@ export function estimateDasWorkload(options) {
  };
 }

-export function finalizeDasBands(rawMaps, workload, experiment) {
+export function finalizeDasBands(rawMaps, workload, experiment, rawScale = "power") {
+  if (rawScale !== "power" && rawScale !== "score") {
+    throw new RangeError('rawScale must be "power" or "score".');
+  }
  return experiment.frequencyBands.map((band, bandIndex) => {
    const rawMap = rawMaps[bandIndex];
    for (let pointIndex = 0; pointIndex < workload.pointCount; pointIndex += 1) {
@@ -528,7 +612,7 @@ export function finalizeDasBands(rawMaps, workload, experiment) {
    }
    const rawPeak = findPeak(rawMap);
    const displayMap = Float32Array.from(rawMap);
-    let displayScale = "power";
+    let displayScale = rawScale;
    if (experiment.beamforming.normalization === "peak" && rawPeak.value > 0) {
      displayScale = "normalized";
      for (let pointIndex = 0; pointIndex < workload.pointCount; pointIndex += 1) {
@@ -540,12 +624,17 @@ export function finalizeDasBands(rawMaps, workload, experiment) {
      color: band.color,
      bins: workload.bandBins[bandIndex],
      rawMap,
+      rawScale,
      displayMap,
      displayScale,
      peak: {
        index: rawPeak.index,
        position: scanIndexToPosition(rawPeak.index, experiment.scan),
-        rawPower: rawPeak.value,
+        rawValue: rawPeak.value,
+        rawScale,
+        ...(rawScale === "power"
+          ? { rawPower: rawPeak.value }
+          : { rawScore: rawPeak.value }),
      },
    };
  });
@@ -566,7 +655,14 @@ async function computeDasMapInternal(options, analysisPlan) {
    maximumSpectrumBatchBins = Number.MAX_SAFE_INTEGER,
    pointBatchSize = 64,
    stftTransformBatchSize = 32,
+    phatRelativeFloor = DEFAULT_PHAT_RELATIVE_FLOOR,
  } = options;
+  const algorithm = analysisPlan?.algorithm ?? requireLocalizationAlgorithmDefinition(
+    experiment.beamforming?.algorithm ?? "das",
+  );
+  if (!isDasAlgorithm(algorithm.id)) {
+    throw new RangeError(`computeDasMap does not implement ${algorithm.id}.`);
+  }
  if (channels.length !== arrayDefinition.microphones.length) {
    throw new Error(`채널 수 ${channels.length}와 마이크 수 ${arrayDefinition.microphones.length}가 다릅니다.`);
  }
@@ -685,6 +781,8 @@ async function computeDasMapInternal(options, analysisPlan) {
        steeringImaginary: analysisPlan?.steeringImaginary,
        signal,
        pointBatchSize,
+        algorithm: algorithm.id,
+        phatRelativeFloor,
      });
      if (analysisPlan) {
        analysisPlan.statistics.beamformingComplexMacs +=
@@ -701,9 +799,10 @@ async function computeDasMapInternal(options, analysisPlan) {
    }
  }

-  const bands = finalizeDasBands(rawMaps, workload, experiment);
+  const bands = finalizeDasBands(rawMaps, workload, experiment, algorithm.rawScale);

  return {
+    algorithm: algorithm.id,
    backend: "cpu",
    frameCount: workload.frameCount,
    microphoneCount: workload.microphoneCount,
@@ -50,8 +50,12 @@ function normalizeRawMap(rawMap, scan) {
  if (!Array.isArray(values) && !ArrayBuffer.isView(values)) {
    throw new TypeError("rawMap must be an Array, a typed array, or a map descriptor with values.");
  }
-  if (descriptor?.scale !== undefined && descriptor.scale !== "power") {
-    throw new RangeError("rawMap scale must be power.");
+  if (
+    descriptor?.scale !== undefined &&
+    descriptor.scale !== "power" &&
+    descriptor.scale !== "score"
+  ) {
+    throw new RangeError("rawMap scale must be power or score.");
  }
  if (descriptor?.rows !== undefined && descriptor.rows !== scan.rows) {
    throw new RangeError("rawMap rows do not match scan.rows.");
@@ -69,6 +73,13 @@ function normalizeRawMap(rawMap, scan) {
  return values;
 }

+function rawMapScale(rawMap) {
+  if (rawMap && !Array.isArray(rawMap) && !ArrayBuffer.isView(rawMap)) {
+    return rawMap.scale ?? "power";
+  }
+  return "power";
+}
+
 function normalizeSources(sources) {
  if (!Array.isArray(sources)) throw new TypeError("sources must be an array.");
  const ids = new Set();
@@ -420,12 +431,14 @@ function collectHalfPowerIndices(values, scan, peakIndex, halfPowerRatio) {
  return { indices: queue.subarray(0, queueEnd), thresholdValue };
 }

-function measureBeamSpreadFromValues(values, scan, peakIndex, halfPowerRatio) {
+function measureBeamSpreadFromValues(values, scan, peakIndex, halfPowerRatio, mapScale = "power") {
  const component = collectHalfPowerIndices(values, scan, peakIndex, halfPowerRatio);
  const peakPosition = scanIndexToPosition(peakIndex, scan);
  const base = {
    calibrated: false,
-    method: "connected-half-power-power-weighted-covariance",
+    method: mapScale === "score"
+      ? "connected-half-maximum-score-weighted-covariance"
+      : "connected-half-power-power-weighted-covariance",
    halfPowerRatio,
    thresholdValue: component.thresholdValue,
    pixelCount: component.indices.length,
@@ -506,18 +519,28 @@ export function measureBeamSpread(rawMap, scan, peakIndex, halfPowerRatio = 0.5)
    throw new RangeError("peakIndex is outside rawMap.");
  }
  requireRatio("halfPowerRatio", halfPowerRatio, { positive: true });
-  return measureBeamSpreadFromValues(values, scan, peakIndex, halfPowerRatio);
+  return measureBeamSpreadFromValues(
+    values,
+    scan,
+    peakIndex,
+    halfPowerRatio,
+    rawMapScale(rawMap),
+  );
 }

 /**
- * Evaluate one linear-power DAS band. Source z coordinates are projected onto
- * scan.z, matching the image-plane ground-truth convention.
+ * Evaluate one nonnegative spatial power or score map. Source z coordinates
+ * are projected onto scan.z, matching the image-plane ground-truth convention.
 */
-export function evaluateDasBand(options) {
+export function evaluateLocalizationBand(options) {
  if (!options || typeof options !== "object") throw new TypeError("evaluation options are required.");
  const { rawMap, scan } = options;
  validateScan(scan);
  const values = normalizeRawMap(rawMap, scan);
+  const mapScale = options.mapScale ?? rawMapScale(rawMap);
+  if (mapScale !== "power" && mapScale !== "score") {
+    throw new RangeError("mapScale must be power or score.");
+  }
  const sources = normalizeSources(options.sources ?? options.scene?.sources ?? []);
  const truths = sources.map((source) => ({
    sourceId: source.id,
@@ -558,7 +581,13 @@ export function evaluateDasBand(options) {
  const detections = selectDetections(localPeaks, thresholdValue, minimumPeakDistanceM, maximumDetections).map(
    (detection) => ({
      ...detection,
-      beamSpread: measureBeamSpreadFromValues(values, scan, detection.index, halfPowerRatio),
+      beamSpread: measureBeamSpreadFromValues(
+        values,
+        scan,
+        detection.index,
+        halfPowerRatio,
+        mapScale,
+      ),
    }),
  );
  const { sourceMatches, truePositiveCount } = matchTruths(truths, detections, matchToleranceM);
@@ -604,6 +633,7 @@ export function evaluateDasBand(options) {
      mainLobeWidthM: mainLobe?.equivalentDiameterM ?? 0,
    },
    diagnostics: {
+      mapScale,
      mainLobe,
      mainLobeWidthM: mainLobe?.equivalentDiameterM ?? 0,
      gridResolutionM: resolution,
@@ -611,6 +641,11 @@ export function evaluateDasBand(options) {
  };
 }

+/** Backwards-compatible name for callers that evaluate conventional DAS maps. */
+export function evaluateDasBand(options) {
+  return evaluateLocalizationBand(options);
+}
+
 /** Select only metric names accepted by result.schema.json. */
 export function selectResultMetrics(evaluationOrMetrics, metricNames = RESULT_METRIC_KEYS) {
  const metrics = evaluationOrMetrics?.metrics ?? evaluationOrMetrics;
@@ -0,0 +1,783 @@
+import {
+  createBinCalibration,
+  createDistanceMatrix,
+  finalizeDasBands,
+} from "./das.js";
+import { cooperativeYield } from "./cooperative-yield.js";
+import { fftInPlace } from "./fft.js";
+import {
+  binsForBand,
+  createStftWorkspace,
+  extractStftBins,
+  getFrameCount,
+} from "./stft.js";
+
+export const GCC_PHAT_ANALYSIS_PLAN_KIND = "micarrsim.gcc-phat-analysis-plan/1";
+export const DEFAULT_GCC_PHAT_FLOOR_RATIO = 1e-6;
+export const DEFAULT_MAXIMUM_GCC_PHAT_PLAN_BYTES = 512 * 1024 * 1024;
+
+function createAbortError(signal) {
+  if (signal?.reason instanceof Error && signal.reason.name === "AbortError") {
+    return signal.reason;
+  }
+  const error = new Error("GCC-PHAT localization was canceled.");
+  error.name = "AbortError";
+  return error;
+}
+
+function throwIfAborted(signal) {
+  if (signal?.aborted) throw createAbortError(signal);
+}
+
+function requirePositiveFinite(value, label) {
+  if (!Number.isFinite(value) || value <= 0) {
+    throw new RangeError(label + " must be a positive finite number.");
+  }
+  return value;
+}
+
+function requirePositiveInteger(value, label) {
+  if (!Number.isInteger(value) || value < 1) {
+    throw new RangeError(label + " must be a positive integer.");
+  }
+  return value;
+}
+
+function validateArrayDefinition(arrayDefinition) {
+  if (!Array.isArray(arrayDefinition?.microphones) || arrayDefinition.microphones.length < 2) {
+    throw new RangeError("GCC-PHAT requires at least two microphones.");
+  }
+  arrayDefinition.microphones.forEach((microphone, index) => {
+    if (
+      !Array.isArray(microphone?.position) ||
+      microphone.position.length !== 3 ||
+      !microphone.position.every(Number.isFinite)
+    ) {
+      throw new TypeError(
+        "arrayDefinition.microphones[" + index + "].position must be a finite [x, y, z] array.",
+      );
+    }
+  });
+  return arrayDefinition;
+}
+
+function validateConfiguration(options) {
+  const sampleRate = requirePositiveFinite(options.sampleRate, "sampleRate");
+  const arrayDefinition = validateArrayDefinition(options.arrayDefinition);
+  const speedOfSound = requirePositiveFinite(options.scene?.speedOfSound, "scene.speedOfSound");
+  const experiment = options.experiment;
+  if (!experiment?.stft || !experiment?.scan || !Array.isArray(experiment.frequencyBands)) {
+    throw new TypeError("experiment must contain stft, scan, and frequencyBands.");
+  }
+  const fftSize = requirePositiveInteger(experiment.stft.fftSize, "experiment.stft.fftSize");
+  if ((fftSize & (fftSize - 1)) !== 0) {
+    throw new RangeError("experiment.stft.fftSize must be a power of two.");
+  }
+  const hopSize = requirePositiveInteger(experiment.stft.hopSize, "experiment.stft.hopSize");
+  if (hopSize > fftSize) {
+    throw new RangeError("experiment.stft.hopSize must not exceed fftSize.");
+  }
+  const pointCount =
+    requirePositiveInteger(experiment.scan.columns, "experiment.scan.columns") *
+    requirePositiveInteger(experiment.scan.rows, "experiment.scan.rows");
+  if (!Number.isSafeInteger(pointCount)) {
+    throw new RangeError("scan point count exceeds the safe integer range.");
+  }
+  if (experiment.frequencyBands.length === 0) {
+    throw new RangeError("experiment.frequencyBands must not be empty.");
+  }
+  return { sampleRate, arrayDefinition, speedOfSound, experiment, fftSize, hopSize, pointCount };
+}
+
+function validateChannels(channels, microphoneCount) {
+  if (!Array.isArray(channels) || channels.length !== microphoneCount) {
+    throw new Error(
+      "channel count " + (channels?.length ?? 0) +
+      " does not match microphone count " + microphoneCount + ".",
+    );
+  }
+  if (!channels.every((channel) => channel instanceof Float32Array)) {
+    throw new TypeError("every channel must be a Float32Array.");
+  }
+  const sampleCount = channels[0].length;
+  if (sampleCount === 0) throw new RangeError("channels must contain at least one sample.");
+  if (channels.some((channel) => channel.length !== sampleCount)) {
+    throw new Error("all channels must have the same sample count.");
+  }
+  return sampleCount;
+}
+
+function referenceTieBreak(left, right) {
+  if (left.id < right.id) return -1;
+  if (left.id > right.id) return 1;
+  return left.index - right.index;
+}
+
+/**
+ * Select a stable reference without depending on microphone input order.
+ *
+ * A peripheral microphone generally provides a longer TDOA baseline. Exact
+ * geometric ties are resolved by microphone id and finally by input index.
+ */
+export function selectGccPhatReference(arrayDefinition) {
+  validateArrayDefinition(arrayDefinition);
+  const centroid = [0, 0, 0];
+  for (const microphone of arrayDefinition.microphones) {
+    for (let axis = 0; axis < 3; axis += 1) centroid[axis] += microphone.position[axis];
+  }
+  for (let axis = 0; axis < 3; axis += 1) {
+    centroid[axis] /= arrayDefinition.microphones.length;
+  }
+
+  let selected;
+  arrayDefinition.microphones.forEach((microphone, index) => {
+    const descriptor = {
+      index,
+      id: String(microphone.id ?? ""),
+      microphone,
+      distanceSquared:
+        (microphone.position[0] - centroid[0]) ** 2 +
+        (microphone.position[1] - centroid[1]) ** 2 +
+        (microphone.position[2] - centroid[2]) ** 2,
+    };
+    if (!selected) {
+      selected = descriptor;
+      return;
+    }
+    if (
+      descriptor.distanceSquared > selected.distanceSquared ||
+      (
+        descriptor.distanceSquared === selected.distanceSquared &&
+        referenceTieBreak(descriptor, selected) < 0
+      )
+    ) {
+      selected = descriptor;
+    }
+  });
+
+  return Object.freeze({
+    index: selected.index,
+    id: selected.id,
+    microphone: selected.microphone,
+  });
+}
+
+function createBandGeometry(configuration) {
+  const bandBins = configuration.experiment.frequencyBands.map((band) =>
+    binsForBand(band, configuration.sampleRate, configuration.fftSize)
+  );
+  const selectedBins = [...new Set(bandBins.flat())].sort((left, right) => left - right);
+  return { bandBins, selectedBins };
+}
+
+function maximumGeometricLagSamples({
+  distances,
+  pointCount,
+  microphoneCount,
+  referenceIndex,
+  sampleRate,
+  speedOfSound,
+}) {
+  let maximumLagSamples = 0;
+  for (let pointIndex = 0; pointIndex < pointCount; pointIndex += 1) {
+    const distanceOffset = pointIndex * microphoneCount;
+    const referenceDistance = distances[distanceOffset + referenceIndex];
+    for (let microphoneIndex = 0; microphoneIndex < microphoneCount; microphoneIndex += 1) {
+      if (microphoneIndex === referenceIndex) continue;
+      const lagSamples =
+        Math.abs(distances[distanceOffset + microphoneIndex] - referenceDistance) *
+        sampleRate / speedOfSound;
+      maximumLagSamples = Math.max(maximumLagSamples, lagSamples);
+    }
+  }
+  return maximumLagSamples;
+}
+
+function assertUnaliasedLag(maximumLagSamples, fftSize) {
+  const maximumUnaliasedLag = fftSize / 2;
+  if (maximumLagSamples > maximumUnaliasedLag) {
+    throw new RangeError(
+      "GCC-PHAT maximum absolute geometric lag " + maximumLagSamples.toFixed(3) +
+      " samples exceeds fftSize/2 (" + maximumUnaliasedLag +
+      "); increase fftSize or reduce the array aperture or scan range.",
+    );
+  }
+}
+
+function createGeometry(configuration, distances) {
+  const microphoneCount = configuration.arrayDefinition.microphones.length;
+  const reference = selectGccPhatReference(configuration.arrayDefinition);
+  const pairMicrophoneIndexes = [];
+  for (let microphoneIndex = 0; microphoneIndex < microphoneCount; microphoneIndex += 1) {
+    if (microphoneIndex !== reference.index) pairMicrophoneIndexes.push(microphoneIndex);
+  }
+  const resolvedDistances =
+    distances ?? createDistanceMatrix(configuration.arrayDefinition, configuration.experiment.scan);
+  const maximumLagSamples = maximumGeometricLagSamples({
+    distances: resolvedDistances,
+    pointCount: configuration.pointCount,
+    microphoneCount,
+    referenceIndex: reference.index,
+    sampleRate: configuration.sampleRate,
+    speedOfSound: configuration.speedOfSound,
+  });
+  assertUnaliasedLag(maximumLagSamples, configuration.fftSize);
+  return {
+    microphoneCount,
+    reference,
+    pairMicrophoneIndexes,
+    pairCount: pairMicrophoneIndexes.length,
+    distances: resolvedDistances,
+    maximumLagSamples,
+    ...createBandGeometry(configuration),
+  };
+}
+
+function requirePhatFloorRatio(value) {
+  const ratio = value ?? DEFAULT_GCC_PHAT_FLOOR_RATIO;
+  if (!Number.isFinite(ratio) || ratio < 0 || ratio > 1) {
+    throw new RangeError("phatFloorRatio must be a finite number from 0 through 1.");
+  }
+  return ratio;
+}
+
+function estimateWithGeometry(options, configuration, geometry) {
+  const sampleCount = validateChannels(options.channels, geometry.microphoneCount);
+  const frameCount = getFrameCount(
+    sampleCount,
+    configuration.fftSize,
+    configuration.hopSize,
+  );
+  const pairCount = geometry.pairCount;
+  const bandCount = geometry.bandBins.length;
+  const bandBinCount = geometry.bandBins.reduce((sum, bins) => sum + bins.length, 0);
+  const selectedBinCount = geometry.selectedBins.length;
+  const stftTransformCount = frameCount * geometry.microphoneCount;
+  const inverseFftCount = pairCount * bandCount;
+  const fftButterfliesPerTransform =
+    (configuration.fftSize / 2) * Math.log2(configuration.fftSize);
+  const stftButterflyCount = stftTransformCount * fftButterfliesPerTransform;
+  const inverseFftButterflyCount = inverseFftCount * fftButterfliesPerTransform;
+  const crossSpectrumSampleCount = pairCount * frameCount * bandBinCount;
+  // The relative-floor pass finds the maximum magnitude, then the PHAT pass
+  // recomputes and accumulates each complex cross-spectrum sample.
+  const crossSpectrumComplexOpCount = crossSpectrumSampleCount * 2;
+  const scanLookupCount = configuration.pointCount * pairCount * bandCount;
+  const dasEquivalentComplexMacCount =
+    configuration.pointCount * geometry.microphoneCount * frameCount * selectedBinCount;
+  const distanceBytes =
+    configuration.pointCount * geometry.microphoneCount * Float32Array.BYTES_PER_ELEMENT;
+  const stftWorkspaceBytes =
+    configuration.fftSize * Float64Array.BYTES_PER_ELEMENT * 3;
+  const correlationWorkspaceBytes =
+    configuration.fftSize * Float64Array.BYTES_PER_ELEMENT * 2;
+  const spectrumBytes =
+    selectedBinCount * frameCount * geometry.microphoneCount *
+    Float32Array.BYTES_PER_ELEMENT * 2;
+  const calibrationPhaseBytes =
+    selectedBinCount * geometry.microphoneCount * Float64Array.BYTES_PER_ELEMENT;
+  const outputBytes =
+    bandCount * configuration.pointCount * Float32Array.BYTES_PER_ELEMENT * 2;
+  const retainedPlanBytes =
+    options.analysisPlan?.retainedBytes ??
+    distanceBytes + stftWorkspaceBytes + correlationWorkspaceBytes;
+  const estimatedWorkingBytes =
+    retainedPlanBytes + spectrumBytes + calibrationPhaseBytes + outputBytes;
+
+  return {
+    bandBins: geometry.bandBins,
+    selectedBins: geometry.selectedBins,
+    selectedBinCount,
+    bandBinCount,
+    frameCount,
+    microphoneCount: geometry.microphoneCount,
+    pointCount: configuration.pointCount,
+    bandCount,
+    pairCount,
+    referenceMicrophoneId: geometry.reference.id,
+    referenceMicrophoneIndex: geometry.reference.index,
+    maximumLagSamples: geometry.maximumLagSamples,
+    stftTransformCount,
+    inverseFftCount,
+    stftButterflyCount,
+    inverseFftButterflyCount,
+    crossSpectrumSampleCount,
+    crossSpectrumComplexOpCount,
+    crossSpectrumCount: crossSpectrumComplexOpCount,
+    complexMacCount: crossSpectrumComplexOpCount,
+    scanLookupCount,
+    operationCount: crossSpectrumComplexOpCount + scanLookupCount,
+    dasEquivalentComplexMacCount,
+    distanceBytes,
+    stftWorkspaceBytes,
+    correlationWorkspaceBytes,
+    spectrumBytes,
+    calibrationPhaseBytes,
+    outputBytes,
+    retainedPlanBytes,
+    estimatedWorkingBytes,
+  };
+}
+
+/**
+ * Estimate CPU work for one reference-pair GCC-PHAT analysis.
+ */
+export function estimateGccPhatWorkload(options) {
+  if (!options || typeof options !== "object") {
+    throw new TypeError("options must be an object.");
+  }
+  const configuration = validateConfiguration(options);
+  let geometry;
+  if (options.analysisPlan) {
+    assertCompatibleAnalysisPlan(options.analysisPlan, options);
+    geometry = options.analysisPlan;
+  } else {
+    geometry = createGeometry(configuration);
+  }
+  return estimateWithGeometry(options, configuration, geometry);
+}
+
+/**
+ * Retain geometry and FFT workspaces for repeated analyses with one immutable
+ * array/scan/band configuration. Plans are stateful and cannot run concurrently.
+ */
+export function createGccPhatAnalysisPlan(options) {
+  if (!options || typeof options !== "object") {
+    throw new TypeError("options must be an object.");
+  }
+  const configuration = validateConfiguration(options);
+  const maximumPlanBytes =
+    options.maximumPlanBytes ?? DEFAULT_MAXIMUM_GCC_PHAT_PLAN_BYTES;
+  if (!Number.isSafeInteger(maximumPlanBytes) || maximumPlanBytes <= 0) {
+    throw new RangeError("maximumPlanBytes must be a positive safe integer.");
+  }
+
+  const microphoneCount = configuration.arrayDefinition.microphones.length;
+  const pointMicrophonePairs = configuration.pointCount * microphoneCount;
+  const distanceBytes = pointMicrophonePairs * Float32Array.BYTES_PER_ELEMENT;
+  const stftWorkspaceBytes =
+    configuration.fftSize * Float64Array.BYTES_PER_ELEMENT * 3;
+  const correlationWorkspaceBytes =
+    configuration.fftSize * Float64Array.BYTES_PER_ELEMENT * 2;
+  const retainedBytes = distanceBytes + stftWorkspaceBytes + correlationWorkspaceBytes;
+  if (
+    !Number.isSafeInteger(pointMicrophonePairs) ||
+    !Number.isSafeInteger(retainedBytes) ||
+    retainedBytes > maximumPlanBytes
+  ) {
+    throw new RangeError(
+      "GCC-PHAT analysis plan requires " + retainedBytes.toLocaleString() +
+      " bytes, above maximumPlanBytes " + maximumPlanBytes.toLocaleString() + ".",
+    );
+  }
+
+  const distances = createDistanceMatrix(
+    configuration.arrayDefinition,
+    configuration.experiment.scan,
+  );
+  const geometry = createGeometry(configuration, distances);
+  const plan = {
+    kind: GCC_PHAT_ANALYSIS_PLAN_KIND,
+    sampleRate: configuration.sampleRate,
+    arrayDefinition: configuration.arrayDefinition,
+    scene: options.scene,
+    experiment: configuration.experiment,
+    fftSize: configuration.fftSize,
+    hopSize: configuration.hopSize,
+    pointCount: configuration.pointCount,
+    maximumPlanBytes,
+    retainedBytes,
+    distanceBytes,
+    stftWorkspaceBytes,
+    correlationWorkspaceBytes,
+    stftWorkspace: createStftWorkspace(configuration.fftSize),
+    correlationReal: new Float64Array(configuration.fftSize),
+    correlationImaginary: new Float64Array(configuration.fftSize),
+    busy: false,
+    statistics: {
+      analysisAttempts: 0,
+      completedAnalyses: 0,
+      distanceMatrixBuilds: 1,
+      distanceEvaluations: pointMicrophonePairs,
+      avoidedDistanceEvaluations: 0,
+      stftWorkspaceAllocations: 1,
+      stftWorkspaceUses: 0,
+      avoidedStftWorkspaceAllocations: 0,
+      correlationWorkspaceAllocations: 1,
+      correlationWorkspaceUses: 0,
+      avoidedCorrelationWorkspaceAllocations: 0,
+      stftTransforms: 0,
+      inverseFftTransforms: 0,
+      crossSpectrumOperations: 0,
+      scanLookups: 0,
+    },
+    ...geometry,
+  };
+  return plan;
+}
+
+function assertCompatibleAnalysisPlan(plan, options) {
+  if (plan?.kind !== GCC_PHAT_ANALYSIS_PLAN_KIND) {
+    throw new TypeError(
+      "analysisPlan must be created by createGccPhatAnalysisPlan().",
+    );
+  }
+  if (
+    plan.sampleRate !== options.sampleRate ||
+    plan.arrayDefinition !== options.arrayDefinition ||
+    plan.scene !== options.scene ||
+    plan.experiment !== options.experiment
+  ) {
+    throw new Error(
+      "analysisPlan must be used with the exact configuration objects that created it.",
+    );
+  }
+}
+
+function analysisCacheSummary(plan) {
+  const statistics = plan.statistics;
+  return {
+    analysisAttempts: statistics.analysisAttempts,
+    completedAnalyses: statistics.completedAnalyses,
+    distanceMatrixBuilds: statistics.distanceMatrixBuilds,
+    distanceEvaluations: statistics.distanceEvaluations,
+    avoidedDistanceEvaluations: statistics.avoidedDistanceEvaluations,
+    stftWorkspaceAllocations: statistics.stftWorkspaceAllocations,
+    stftWorkspaceUses: statistics.stftWorkspaceUses,
+    avoidedStftWorkspaceAllocations: statistics.avoidedStftWorkspaceAllocations,
+    correlationWorkspaceAllocations: statistics.correlationWorkspaceAllocations,
+    correlationWorkspaceUses: statistics.correlationWorkspaceUses,
+    avoidedCorrelationWorkspaceAllocations:
+      statistics.avoidedCorrelationWorkspaceAllocations,
+    stftTransforms: statistics.stftTransforms,
+    inverseFftTransforms: statistics.inverseFftTransforms,
+    crossSpectrumOperations: statistics.crossSpectrumOperations,
+    scanLookups: statistics.scanLookups,
+    retainedBytes: plan.retainedBytes,
+    maximumPlanBytes: plan.maximumPlanBytes,
+  };
+}
+
+function circularLinearSample(values, lagSamples) {
+  const length = values.length;
+  const wrapped = ((lagSamples % length) + length) % length;
+  const firstIndex = Math.floor(wrapped);
+  const fraction = wrapped - firstIndex;
+  const secondIndex = (firstIndex + 1) % length;
+  return values[firstIndex] * (1 - fraction) + values[secondIndex] * fraction;
+}
+
+function fillBandPairCorrelation({
+  correlationReal,
+  correlationImaginary,
+  bins,
+  stft,
+  microphoneIndex,
+  referenceIndex,
+  phasesByBin,
+  phatFloorRatio,
+}) {
+  correlationReal.fill(0);
+  correlationImaginary.fill(0);
+  const microphoneCount = stft.microphoneCount;
+  let maximumMagnitude = 0;
+
+  for (const bin of bins) {
+    const spectrum = stft.spectra.get(bin);
+    for (let frameIndex = 0; frameIndex < stft.frameCount; frameIndex += 1) {
+      const offset = frameIndex * microphoneCount;
+      const microphoneReal = spectrum.real[offset + microphoneIndex];
+      const microphoneImaginary = spectrum.imaginary[offset + microphoneIndex];
+      const referenceReal = spectrum.real[offset + referenceIndex];
+      const referenceImaginary = spectrum.imaginary[offset + referenceIndex];
+      const crossReal =
+        microphoneReal * referenceReal + microphoneImaginary * referenceImaginary;
+      const crossImaginary =
+        microphoneImaginary * referenceReal - microphoneReal * referenceImaginary;
+      maximumMagnitude = Math.max(
+        maximumMagnitude,
+        Math.hypot(crossReal, crossImaginary),
+      );
+    }
+  }
+
+  const magnitudeFloor = maximumMagnitude * phatFloorRatio;
+  if (!(maximumMagnitude > 0)) {
+    fftInPlace(correlationReal, correlationImaginary, true);
+    return;
+  }
+
+  for (const bin of bins) {
+    const spectrum = stft.spectra.get(bin);
+    const phases = phasesByBin.get(bin);
+    const correctionPhase = phases[referenceIndex] - phases[microphoneIndex];
+    const correctionReal = Math.cos(correctionPhase);
+    const correctionImaginary = Math.sin(correctionPhase);
+    let accumulatedReal = 0;
+    let accumulatedImaginary = 0;
+
+    for (let frameIndex = 0; frameIndex < stft.frameCount; frameIndex += 1) {
+      const offset = frameIndex * microphoneCount;
+      const microphoneReal = spectrum.real[offset + microphoneIndex];
+      const microphoneImaginary = spectrum.imaginary[offset + microphoneIndex];
+      const referenceReal = spectrum.real[offset + referenceIndex];
+      const referenceImaginary = spectrum.imaginary[offset + referenceIndex];
+      const crossReal =
+        microphoneReal * referenceReal + microphoneImaginary * referenceImaginary;
+      const crossImaginary =
+        microphoneImaginary * referenceReal - microphoneReal * referenceImaginary;
+      const magnitude = Math.hypot(crossReal, crossImaginary);
+      const denominator = Math.max(magnitude, magnitudeFloor);
+      if (!(denominator > 0)) continue;
+      const normalizedReal = crossReal / denominator;
+      const normalizedImaginary = crossImaginary / denominator;
+      accumulatedReal +=
+        normalizedReal * correctionReal - normalizedImaginary * correctionImaginary;
+      accumulatedImaginary +=
+        normalizedReal * correctionImaginary + normalizedImaginary * correctionReal;
+    }
+
+    const averagedReal = accumulatedReal / stft.frameCount;
+    const averagedImaginary = accumulatedImaginary / stft.frameCount;
+    correlationReal[bin] = averagedReal;
+    correlationImaginary[bin] =
+      bin === 0 || bin === correlationReal.length / 2 ? 0 : averagedImaginary;
+    if (bin > 0 && bin < correlationReal.length / 2) {
+      const negativeBin = correlationReal.length - bin;
+      correlationReal[negativeBin] = averagedReal;
+      correlationImaginary[negativeBin] = -averagedImaginary;
+    }
+  }
+
+  fftInPlace(correlationReal, correlationImaginary, true);
+}
+
+function shiftAndSquareScores(rawMaps) {
+  for (const rawMap of rawMaps) {
+    let minimum = Infinity;
+    for (const value of rawMap) minimum = Math.min(minimum, value);
+    for (let pointIndex = 0; pointIndex < rawMap.length; pointIndex += 1) {
+      const shifted = rawMap[pointIndex] - minimum;
+      rawMap[pointIndex] = shifted * shifted;
+    }
+  }
+}
+
+async function computeGccPhatMapInternal(options, configuration, geometry, plan) {
+  const {
+    channels,
+    signal,
+    onProgress,
+    maximumWorkingBytes = 512 * 1024 * 1024,
+    maximumFftButterflies = 1_000_000_000,
+    pointBatchSize = 256,
+    stftTransformBatchSize = 128,
+  } = options;
+  requirePositiveInteger(pointBatchSize, "pointBatchSize");
+  requirePositiveInteger(stftTransformBatchSize, "stftTransformBatchSize");
+  if (!Number.isSafeInteger(maximumWorkingBytes) || maximumWorkingBytes <= 0) {
+    throw new RangeError("maximumWorkingBytes must be a positive safe integer.");
+  }
+  const phatFloorRatio = requirePhatFloorRatio(
+    options.phatFloorRatio ??
+    options.phatRelativeFloor ??
+    configuration.experiment.beamforming?.phatFloorRatio,
+  );
+  const workload = estimateWithGeometry(options, configuration, geometry);
+  if (workload.estimatedWorkingBytes > maximumWorkingBytes) {
+    throw new RangeError(
+      "GCC-PHAT estimated working set " +
+      (workload.estimatedWorkingBytes / 1024 ** 2).toFixed(1) +
+      " MiB exceeds maximumWorkingBytes " +
+      (maximumWorkingBytes / 1024 ** 2).toFixed(1) + " MiB.",
+    );
+  }
+  const totalFftButterflies =
+    workload.stftButterflyCount + workload.inverseFftButterflyCount;
+  if (totalFftButterflies > maximumFftButterflies) {
+    throw new RangeError(
+      "GCC-PHAT estimated FFT butterfly count " +
+      Math.ceil(totalFftButterflies).toLocaleString() +
+      " exceeds maximumFftButterflies " +
+      maximumFftButterflies.toLocaleString() + ".",
+    );
+  }
+
+  throwIfAborted(signal);
+  if (plan) {
+    plan.statistics.stftWorkspaceUses += 1;
+    plan.statistics.avoidedStftWorkspaceAllocations = Math.max(
+      0,
+      plan.statistics.stftWorkspaceUses - 1,
+    );
+    plan.statistics.correlationWorkspaceUses += 1;
+    plan.statistics.avoidedCorrelationWorkspaceAllocations = Math.max(
+      0,
+      plan.statistics.correlationWorkspaceUses - 1,
+    );
+  }
+  const stft = await extractStftBins(channels, {
+    fftSize: configuration.fftSize,
+    hopSize: configuration.hopSize,
+    bins: geometry.selectedBins,
+    workspace: plan?.stftWorkspace,
+    transformBatchSize: stftTransformBatchSize,
+    signal,
+    onProgress: ({ completedTransforms, totalTransforms }) =>
+      onProgress?.({
+        stage: "stft",
+        algorithm: "gcc-phat",
+        completedTransforms,
+        totalTransforms,
+      }),
+  });
+  if (plan) {
+    plan.statistics.stftTransforms += workload.stftTransformCount;
+  }
+  await cooperativeYield();
+  throwIfAborted(signal);
+
+  const phasesByBin = new Map();
+  for (const bin of geometry.selectedBins) {
+    const calibration = createBinCalibration({
+      arrayDefinition: configuration.arrayDefinition,
+      bin,
+      sampleRate: configuration.sampleRate,
+      fftSize: configuration.fftSize,
+      compensateFrequencyResponse:
+        configuration.experiment.beamforming?.compensateFrequencyResponse !== false,
+      maxCompensationDb:
+        configuration.experiment.beamforming?.maxCompensationDb,
+      includeAmplitude: false,
+    });
+    phasesByBin.set(bin, calibration.phases);
+  }
+
+  const correlationReal =
+    plan?.correlationReal ?? new Float64Array(configuration.fftSize);
+  const correlationImaginary =
+    plan?.correlationImaginary ?? new Float64Array(configuration.fftSize);
+  const rawMaps = geometry.bandBins.map(() => new Float32Array(configuration.pointCount));
+  const totalPairs = geometry.pairCount * geometry.bandBins.length;
+  let completedPairs = 0;
+
+  for (let bandIndex = 0; bandIndex < geometry.bandBins.length; bandIndex += 1) {
+    const bins = geometry.bandBins[bandIndex];
+    const rawMap = rawMaps[bandIndex];
+    for (const microphoneIndex of geometry.pairMicrophoneIndexes) {
+      throwIfAborted(signal);
+      fillBandPairCorrelation({
+        correlationReal,
+        correlationImaginary,
+        bins,
+        stft,
+        microphoneIndex,
+        referenceIndex: geometry.reference.index,
+        phasesByBin,
+        phatFloorRatio,
+      });
+      if (plan) {
+        plan.statistics.inverseFftTransforms += 1;
+        plan.statistics.crossSpectrumOperations +=
+          bins.length * stft.frameCount * 2;
+      }
+
+      for (let pointIndex = 0; pointIndex < configuration.pointCount; pointIndex += 1) {
+        const distanceOffset = pointIndex * geometry.microphoneCount;
+        const predictedLagSamples =
+          (
+            geometry.distances[distanceOffset + microphoneIndex] -
+            geometry.distances[distanceOffset + geometry.reference.index]
+          ) *
+          configuration.sampleRate / configuration.speedOfSound;
+        rawMap[pointIndex] += circularLinearSample(
+          correlationReal,
+          predictedLagSamples,
+        );
+        if (
+          (pointIndex + 1) % pointBatchSize === 0 &&
+          pointIndex + 1 < configuration.pointCount
+        ) {
+          await cooperativeYield();
+          throwIfAborted(signal);
+        }
+      }
+      if (plan) plan.statistics.scanLookups += configuration.pointCount;
+      completedPairs += 1;
+      onProgress?.({
+        stage: "localization",
+        algorithm: "gcc-phat",
+        completedPairs,
+        totalPairs,
+      });
+      if (completedPairs < totalPairs) await cooperativeYield();
+    }
+  }
+
+  throwIfAborted(signal);
+  shiftAndSquareScores(rawMaps);
+  const bands = finalizeDasBands(
+    rawMaps,
+    workload,
+    configuration.experiment,
+    "score",
+  );
+  return {
+    backend: "cpu",
+    algorithm: "gcc-phat",
+    frameCount: workload.frameCount,
+    microphoneCount: workload.microphoneCount,
+    pointCount: workload.pointCount,
+    referenceMicrophoneId: geometry.reference.id,
+    referenceMicrophoneIndex: geometry.reference.index,
+    pairCount: geometry.pairCount,
+    phatFloorRatio,
+    maximumLagSamples: geometry.maximumLagSamples,
+    workload,
+    bands,
+    ...(plan ? { analysisCache: analysisCacheSummary(plan) } : {}),
+  };
+}
+
+/**
+ * Build band-limited reference-pair GCC-PHAT maps on the CPU.
+ *
+ * For each pair and band, corrected PHAT cross spectra are averaged over STFT
+ * frames, transformed once to circular correlation, and sampled at the
+ * near-field geometric delay of each scan point.
+ */
+export async function computeGccPhatMap(options) {
+  if (!options || typeof options !== "object") {
+    throw new TypeError("options must be an object.");
+  }
+  const configuration = validateConfiguration(options);
+  const plan = options.analysisPlan;
+  if (!plan) {
+    const geometry = createGeometry(configuration);
+    return computeGccPhatMapInternal(options, configuration, geometry, undefined);
+  }
+
+  assertCompatibleAnalysisPlan(plan, options);
+  if (plan.busy) {
+    throw new Error("analysisPlan is already in use by another GCC-PHAT analysis.");
+  }
+  plan.busy = true;
+  plan.statistics.analysisAttempts += 1;
+  if (plan.statistics.analysisAttempts > 1) {
+    plan.statistics.avoidedDistanceEvaluations +=
+      plan.pointCount * plan.microphoneCount;
+  }
+  try {
+    const result = await computeGccPhatMapInternal(
+      options,
+      configuration,
+      plan,
+      plan,
+    );
+    plan.statistics.completedAnalyses += 1;
+    result.analysisCache = analysisCacheSummary(plan);
+    return result;
+  } finally {
+    plan.busy = false;
+  }
+}
@@ -4,6 +4,9 @@ export * from "./environment.js";
 export * from "./fft.js";
 export * from "./frequency-response.js";
 export * from "./fractional-delay.js";
+export * from "./gcc-phat.js";
+export * from "./localization.js";
+export * from "./localization-algorithms.js";
 export * from "./math.js";
 export * from "./mixdown.js";
 export * from "./noise.js";
@@ -0,0 +1,52 @@
+export const DEFAULT_LOCALIZATION_ALGORITHM = "das";
+export const DEFAULT_PHAT_RELATIVE_FLOOR = 1e-6;
+
+function definition(id, label, rawScale, spectralWeighting, backends) {
+  return Object.freeze({
+    id,
+    label,
+    rawScale,
+    spectralWeighting,
+    backends: Object.freeze([...backends]),
+  });
+}
+
+const ALGORITHM_DEFINITIONS = Object.freeze([
+  definition("das", "DAS", "power", "none", ["cpu", "webgpu"]),
+  definition("das-phat", "DAS-PHAT", "score", "phat", ["cpu", "webgpu"]),
+  definition("gcc-phat", "GCC-PHAT", "score", "phat", ["cpu"]),
+]);
+
+const ALGORITHM_BY_ID = new Map(
+  ALGORITHM_DEFINITIONS.map((algorithm) => [algorithm.id, algorithm]),
+);
+
+export const LOCALIZATION_ALGORITHM_IDS = Object.freeze(
+  ALGORITHM_DEFINITIONS.map((algorithm) => algorithm.id),
+);
+
+export function localizationAlgorithmDefinition(id = DEFAULT_LOCALIZATION_ALGORITHM) {
+  return ALGORITHM_BY_ID.get(id);
+}
+
+export function requireLocalizationAlgorithmDefinition(
+  id = DEFAULT_LOCALIZATION_ALGORITHM,
+) {
+  const algorithm = localizationAlgorithmDefinition(id);
+  if (!algorithm) {
+    throw new RangeError(`unsupported localization algorithm: ${id}`);
+  }
+  return algorithm;
+}
+
+export function isDasAlgorithm(id = DEFAULT_LOCALIZATION_ALGORITHM) {
+  return id === "das" || id === "das-phat";
+}
+
+export function localizationAlgorithmLabel(id = DEFAULT_LOCALIZATION_ALGORITHM) {
+  return localizationAlgorithmDefinition(id)?.label ?? String(id);
+}
+
+export function localizationAlgorithmSupportsBackend(id, backend) {
+  return localizationAlgorithmDefinition(id)?.backends.includes(backend) ?? false;
+}
@@ -0,0 +1,53 @@
+import {
+  computeDasMap,
+  createDasAnalysisPlan,
+  estimateDasWorkload,
+} from "./das.js";
+import {
+  computeGccPhatMap,
+  createGccPhatAnalysisPlan,
+  estimateGccPhatWorkload,
+} from "./gcc-phat.js";
+import {
+  isDasAlgorithm,
+  requireLocalizationAlgorithmDefinition,
+} from "./localization-algorithms.js";
+
+function requestedAlgorithm(options) {
+  return requireLocalizationAlgorithmDefinition(
+    options?.experiment?.beamforming?.algorithm,
+  );
+}
+
+/**
+ * Estimate implementation-level work for whichever localization algorithm the
+ * experiment requests.
+ */
+export function estimateLocalizationWorkload(options) {
+  const algorithm = requestedAlgorithm(options);
+  return isDasAlgorithm(algorithm.id)
+    ? estimateDasWorkload(options)
+    : estimateGccPhatWorkload(options);
+}
+
+/**
+ * Compile immutable geometry/workspace state for repeated CPU analyses.
+ */
+export function createLocalizationAnalysisPlan(options) {
+  const algorithm = requestedAlgorithm(options);
+  return isDasAlgorithm(algorithm.id)
+    ? createDasAnalysisPlan(options)
+    : createGccPhatAnalysisPlan(options);
+}
+
+/**
+ * Compute a CPU localization map using the algorithm selected by the
+ * experiment. The returned `algorithm` and each band's `rawScale` make the
+ * result semantics explicit.
+ */
+export async function computeLocalizationMap(options) {
+  const algorithm = requestedAlgorithm(options);
+  return isDasAlgorithm(algorithm.id)
+    ? computeDasMap(options)
+    : computeGccPhatMap(options);
+}
@@ -1,5 +1,12 @@
-import { computeDasMap, createDasAnalysisPlan } from "./das.js";
 import { findLocalPeaks } from "./evaluation.js";
+import {
+  computeLocalizationMap,
+  createLocalizationAnalysisPlan,
+} from "./localization.js";
+import {
+  isDasAlgorithm,
+  requireLocalizationAlgorithmDefinition,
+} from "./localization-algorithms.js";
 import {
  DEFAULT_MAX_COMPENSATION_DB,
  MAX_FREQUENCY_RESPONSE_HZ,
@@ -214,6 +221,10 @@ function throwIfAborted(signal) {
 }

 export function detectBandPeaks(rawMap, scan, options = {}) {
+  const rawScale = options.rawScale ?? rawMap?.scale ?? "power";
+  if (rawScale !== "power" && rawScale !== "score") {
+    throw new RangeError('peak map rawScale must be "power" or "score".');
+  }
  const peakDetection = copyPeakDetection(options, scan);
  const localPeaks = findLocalPeaks(rawMap, scan);
  const threshold = (localPeaks[0]?.value ?? 0) * peakDetection.thresholdRatio;
@@ -235,7 +246,9 @@ export function detectBandPeaks(rawMap, scan, options = {}) {
      rank: detections.length + 1,
      index: peak.index,
      position: [...peak.position],
-      rawPower: peak.value,
+      rawValue: peak.value,
+      rawScale,
+      ...(rawScale === "power" ? { rawPower: peak.value } : {}),
    });
    if (detections.length === peakDetection.maximumDetections) break;
  }
@@ -244,7 +257,10 @@ export function detectBandPeaks(rawMap, scan, options = {}) {

 function locationsFromBands(bands, scan, peakDetection) {
  return bands.flatMap((band) =>
-    detectBandPeaks(band.rawMap, scan, peakDetection).map((peak) => ({
+    detectBandPeaks(band.rawMap, scan, {
+      ...peakDetection,
+      rawScale: band.rawScale,
+    }).map((peak) => ({
      bandId: band.id,
      ...peak,
    })),
@@ -287,6 +303,13 @@ export function createLocalizationPlan(config) {
      config.beamforming?.maxCompensationDb ??
      DEFAULT_MAX_COMPENSATION_DB,
  );
+  const algorithm = requireLocalizationAlgorithmDefinition(
+    config.algorithm ?? config.beamforming?.algorithm,
+  );
+  const phatRelativeFloor =
+    config.phatRelativeFloor ?? config.beamforming?.phatRelativeFloor;
+  const phatFloorRatio =
+    config.phatFloorRatio ?? config.beamforming?.phatFloorRatio;

  const scene = Object.freeze({ speedOfSound });
  const experiment = Object.freeze({
@@ -294,12 +317,14 @@ export function createLocalizationPlan(config) {
    scan,
    frequencyBands,
    beamforming: Object.freeze({
+      algorithm: algorithm.id,
+      backend: "cpu",
      normalization,
      compensateFrequencyResponse,
      maxCompensationDb,
    }),
  });
-  const dasPlan = createDasAnalysisPlan({
+  const analysisPlan = createLocalizationAnalysisPlan({
    sampleRate,
    arrayDefinition,
    scene,
@@ -310,6 +335,7 @@ export function createLocalizationPlan(config) {

  return Object.freeze({
    kind: REALTIME_LOCALIZATION_PLAN_KIND,
+    algorithm: algorithm.id,
    sampleRate,
    channelCount: arrayDefinition.microphones.length,
    arrayDefinition,
@@ -322,10 +348,15 @@ export function createLocalizationPlan(config) {
    maxCompensationDb,
    scene,
    experiment,
-    dasPlan,
+    analysisPlan,
+    ...(isDasAlgorithm(algorithm.id)
+      ? { dasPlan: analysisPlan }
+      : { gccPhatPlan: analysisPlan }),
    computeOptions: Object.freeze({
      pointBatchSize: DEFAULT_REALTIME_POINT_BATCH_SIZE,
      stftTransformBatchSize: DEFAULT_REALTIME_STFT_TRANSFORM_BATCH_SIZE,
+      ...(phatRelativeFloor !== undefined ? { phatRelativeFloor } : {}),
+      ...(phatFloorRatio !== undefined ? { phatFloorRatio } : {}),
      ...(config.computeOptions ?? {}),
    }),
  });
@@ -350,7 +381,7 @@ export async function localizeMultichannel(options) {
  if (sampleCount === 0) throw new RangeError("channels must contain at least one sample.");
  throwIfAborted(options.signal);
  const processingStartedAt = monotonicNow();
-  const das = await computeDasMap({
+  const localization = await computeLocalizationMap({
    ...plan.computeOptions,
    ...(options.computeOptions ?? {}),
    channels: options.channels,
@@ -358,14 +389,18 @@ export async function localizeMultichannel(options) {
    arrayDefinition: plan.arrayDefinition,
    scene: plan.scene,
    experiment: plan.experiment,
-    analysisPlan: plan.dasPlan,
+    analysisPlan: plan.analysisPlan,
    signal: options.signal,
    onProgress: options.onProgress,
  });
-  const locations = locationsFromBands(das.bands, plan.scan, plan.peakDetection);
+  const locations = locationsFromBands(
+    localization.bands,
+    plan.scan,
+    plan.peakDetection,
+  );
  const processingTimeMs = Math.max(0, monotonicNow() - processingStartedAt);
  return {
-    ...das,
+    ...localization,
    sampleRate: plan.sampleRate,
    sampleCount,
    duration: sampleCount / plan.sampleRate,
@@ -488,6 +523,7 @@ export class StreamingDasLocalizer {
  getState() {
    return {
      sampleRate: this.plan.sampleRate,
+      algorithm: this.plan.algorithm,
      channelCount: this.plan.channelCount,
      analysisWindowSamples: this.analysisWindowSamples,
      analysisHopSamples: this.analysisHopSamples,
@@ -675,6 +711,9 @@ export class StreamingDasLocalizer {
  }
 }

+/** Algorithm-neutral alias; the older class name remains for compatibility. */
+export { StreamingDasLocalizer as StreamingLocalizer };
+
 export function createStreamingLocalizer(config) {
  return new StreamingDasLocalizer(config);
 }
@@ -6,6 +6,7 @@ const MAX_SOURCES = 32;
 const MAX_GRID_SIDE = 512;
 const MAX_FREQUENCY_RESPONSE_POINTS = 64;
 const MAX_FREQUENCY_RESPONSE_HZ = 1_000_000;
+const LOCALIZATION_ALGORITHMS = ["das", "das-phat", "gcc-phat"];

 /** @typedef {{ path: string, message: string }} ValidationIssue */

@@ -436,10 +437,26 @@ function validateExperimentRelationships(value, context = {}) {
    if (value.stft.window !== "hann") addIssue(issues, "$.stft.window", "hann이어야 합니다.");
  }
  if (requireRecord(value.beamforming, "$.beamforming", issues)) {
-    if (value.beamforming.algorithm !== "das") addIssue(issues, "$.beamforming.algorithm", "das이어야 합니다.");
+    if (!LOCALIZATION_ALGORITHMS.includes(value.beamforming.algorithm)) {
+      addIssue(
+        issues,
+        "$.beamforming.algorithm",
+        "das, das-phat 또는 gcc-phat이어야 합니다.",
+      );
+    }
    if (!["auto", "cpu", "webgpu"].includes(value.beamforming.backend)) {
      addIssue(issues, "$.beamforming.backend", "auto, cpu 또는 webgpu여야 합니다.");
    }
+    if (
+      value.beamforming.algorithm === "gcc-phat" &&
+      value.beamforming.backend === "webgpu"
+    ) {
+      addIssue(
+        issues,
+        "$.beamforming.backend",
+        "gcc-phat는 현재 CPU backend만 지원합니다. auto 또는 cpu를 선택하세요.",
+      );
+    }
    requireFinite(value.beamforming.minimumDistance, "$.beamforming.minimumDistance", issues, {
      min: 0,
      max: 10,
@@ -475,6 +492,16 @@ function validateExperimentRelationships(value, context = {}) {
    }
  }
  if (context.arrayDefinition && context.scene) {
+    if (
+      value.beamforming?.algorithm === "gcc-phat" &&
+      context.arrayDefinition.microphones.length < 2
+    ) {
+      addIssue(
+        issues,
+        "$.beamforming.algorithm",
+        "gcc-phat에는 서로 다른 두 개 이상의 마이크가 필요합니다.",
+      );
+    }
    const retainedBufferCount = context.arrayDefinition.microphones.length + context.scene.sources.length + 1;
    const sampleCount = Math.ceil(context.scene.duration * context.scene.sampleRate);
    let maximumDelaySamples = 0;
@@ -519,6 +546,12 @@ function validateResultRelationships(value, context = {}) {
    if (value.experimentId !== context.experiment.id) addIssue(issues, "$.experimentId", "불러온 실험 id와 다릅니다.");
    if (value.arrayId !== context.experiment.arrayId) addIssue(issues, "$.arrayId", "실험의 배열 id와 다릅니다.");
    if (value.sceneId !== context.experiment.sceneId) addIssue(issues, "$.sceneId", "실험의 장면 id와 다릅니다.");
+    if (
+      value.algorithm !== undefined &&
+      value.algorithm !== context.experiment.beamforming?.algorithm
+    ) {
+      addIssue(issues, "$.algorithm", "실험의 위치 추정 알고리즘과 다릅니다.");
+    }
  }
  const sourceIds = new Set(context.scene?.sources?.map((source) => source.id) ?? []);
  const expectedBandIds = new Set(context.experiment?.frequencyBands?.map((band) => band.id) ?? []);
@@ -541,8 +574,8 @@ function validateResultRelationships(value, context = {}) {
          addIssue(issues, `${path}.maps[${mapIndex}]`, "map shape이 실험 scan grid와 다릅니다.");
        }
      });
-      if (isRecord(band.rawMap) && band.rawMap.scale !== "power") {
-        addIssue(issues, `${path}.rawMap.scale`, "rawMap은 power scale이어야 합니다.");
+      if (isRecord(band.rawMap) && !["power", "score"].includes(band.rawMap.scale)) {
+        addIssue(issues, `${path}.rawMap.scale`, "rawMap은 power 또는 score scale이어야 합니다.");
      }
      if (Array.isArray(band.peaks)) {
        band.peaks.forEach((peak, peakIndex) => {
@@ -1,4 +1,9 @@
 import { computeDasMap } from "../acoustics-core/das.js";
+import { computeGccPhatMap } from "../acoustics-core/gcc-phat.js";
+import {
+  isDasAlgorithm,
+  requireLocalizationAlgorithmDefinition,
+} from "../acoustics-core/localization-algorithms.js";
 import { computeDasWebGpu } from "./das.js";
 import { isRecoverableWebGpuError } from "./device.js";

@@ -17,7 +22,7 @@ function throwIfAborted(signal) {
 * Execute the requested DAS backend. `auto` falls back only for typed WebGPU
 * operational errors; invalid inputs and AbortError remain visible to callers.
 */
-export async function computeDasWithBackend(options, dependencies = {}) {
+async function computeDasBackend(options, dependencies = {}) {
  const computeGpu = dependencies.computeGpu ?? computeDasWebGpu;
  const computeCpu = dependencies.computeCpu ?? computeDasMap;
  const requestedBackend = options?.experiment?.beamforming?.backend ?? "auto";
@@ -56,3 +61,48 @@ export async function computeDasWithBackend(options, dependencies = {}) {
    }
  }
 }
+
+/**
+ * Execute a DAS-family algorithm with the requested CPU/WebGPU policy.
+ * Conventional DAS callers can keep using this backwards-compatible entry.
+ */
+export async function computeDasWithBackend(options, dependencies = {}) {
+  const algorithm = requireLocalizationAlgorithmDefinition(
+    options?.experiment?.beamforming?.algorithm,
+  );
+  if (!isDasAlgorithm(algorithm.id)) {
+    throw new RangeError(
+      `computeDasWithBackend does not implement ${algorithm.id}.`,
+    );
+  }
+  return computeDasBackend(options, dependencies);
+}
+
+/**
+ * Execute any registered localization algorithm. GCC-PHAT currently uses its
+ * portable CPU implementation for both `auto` and `cpu`; an explicit WebGPU
+ * request is rejected instead of silently changing the requested backend.
+ */
+export async function computeLocalizationWithBackend(options, dependencies = {}) {
+  const algorithm = requireLocalizationAlgorithmDefinition(
+    options?.experiment?.beamforming?.algorithm,
+  );
+  if (isDasAlgorithm(algorithm.id)) {
+    return computeDasBackend(options, dependencies);
+  }
+
+  const requestedBackend = options?.experiment?.beamforming?.backend ?? "auto";
+  if (requestedBackend === "webgpu") {
+    throw new TypeError(
+      `${algorithm.label} currently supports only the CPU backend; choose auto or cpu.`,
+    );
+  }
+  if (requestedBackend !== "auto" && requestedBackend !== "cpu") {
+    throw new TypeError(`unsupported localization backend: ${requestedBackend}`);
+  }
+  const computeCpu =
+    dependencies.computeGccCpu ??
+    dependencies.computeCpu ??
+    computeGccPhatMap;
+  return computeCpu(options);
+}
@@ -9,6 +9,11 @@ import {
  requireMaxCompensationDb,
 } from "../acoustics-core/frequency-response.js";
 import { dbToLinear } from "../acoustics-core/math.js";
+import {
+  DEFAULT_PHAT_RELATIVE_FLOOR,
+  isDasAlgorithm,
+  requireLocalizationAlgorithmDefinition,
+} from "../acoustics-core/localization-algorithms.js";
 import { extractStftBins } from "../acoustics-core/stft.js";
 import {
  DAS_PARAMS_BYTE_LENGTH,
@@ -72,11 +77,60 @@ function minimumBufferSize(byteLength) {
  return Math.max(4, Math.ceil(byteLength / 4) * 4);
 }

+function requirePhatRelativeFloor(value) {
+  if (!Number.isFinite(value) || value < 0 || value > 1) {
+    throw new RangeError("phatRelativeFloor must lie inside 0..1.");
+  }
+  return value;
+}
+
+function phaseNormalizePackedStft(
+  packed,
+  binCount,
+  frameCount,
+  microphoneCount,
+  relativeFloor,
+) {
+  requirePhatRelativeFloor(relativeFloor);
+  const valuesPerBin = frameCount * microphoneCount;
+  for (let binIndex = 0; binIndex < binCount; binIndex += 1) {
+    const binOffset = binIndex * valuesPerBin * 2;
+    for (let frameIndex = 0; frameIndex < frameCount; frameIndex += 1) {
+      const frameOffset = binOffset + frameIndex * microphoneCount * 2;
+      let maximumMagnitude = 0;
+      for (let microphoneIndex = 0; microphoneIndex < microphoneCount; microphoneIndex += 1) {
+        const valueOffset = frameOffset + microphoneIndex * 2;
+        maximumMagnitude = Math.max(
+          maximumMagnitude,
+          Math.hypot(packed[valueOffset], packed[valueOffset + 1]),
+        );
+      }
+      const magnitudeFloor = maximumMagnitude * relativeFloor;
+      for (let microphoneIndex = 0; microphoneIndex < microphoneCount; microphoneIndex += 1) {
+        const valueOffset = frameOffset + microphoneIndex * 2;
+        const magnitude = Math.hypot(packed[valueOffset], packed[valueOffset + 1]);
+        if (!(magnitude > magnitudeFloor)) {
+          packed[valueOffset] = 0;
+          packed[valueOffset + 1] = 0;
+          continue;
+        }
+        packed[valueOffset] /= magnitude;
+        packed[valueOffset + 1] /= magnitude;
+      }
+    }
+  }
+}
+
 export function packStftBatch(stft, bins, options = {}) {
  if (!stft || !Number.isInteger(stft.frameCount) || !Number.isInteger(stft.microphoneCount)) {
    throw new TypeError("유효한 STFT 결과가 필요합니다.");
  }
  if (!Array.isArray(bins) || bins.length === 0) throw new TypeError("하나 이상의 FFT bin이 필요합니다.");
+  const algorithm = requireLocalizationAlgorithmDefinition(options.algorithm ?? "das");
+  if (!isDasAlgorithm(algorithm.id)) {
+    throw new RangeError(`packStftBatch does not implement ${algorithm.id}.`);
+  }
+  const phaseOnly = algorithm.spectralWeighting === "phat";
  const valuesPerBin = stft.frameCount * stft.microphoneCount;
  const packed = new Float32Array(bins.length * valuesPerBin * 2);
  const microphones = options.microphones;
@@ -105,9 +159,9 @@ export function packStftBatch(stft, bins, options = {}) {
      microphones.forEach((microphone, microphoneIndex) => {
        if (!Array.isArray(microphone.frequencyResponse)) return;
        interpolateFrequencyResponse(microphone.frequencyResponse, frequencyHz, response);
-        const inverseGain = dbToLinear(
-          inverseResponseGainDb(response.gainDb, maxCompensationDb),
-        );
+        const inverseGain = phaseOnly
+          ? 1
+          : dbToLinear(inverseResponseGainDb(response.gainDb, maxCompensationDb));
        const responseOffset = (binIndex * stft.microphoneCount + microphoneIndex) * 2;
        inverseResponses[responseOffset] = inverseGain * Math.cos(response.phaseRadians);
        inverseResponses[responseOffset + 1] = -inverseGain * Math.sin(response.phaseRadians);
@@ -138,6 +192,15 @@ export function packStftBatch(stft, bins, options = {}) {
        inputReal * inverseImaginary + inputImaginary * inverseReal;
    }
  });
+  if (phaseOnly) {
+    phaseNormalizePackedStft(
+      packed,
+      bins.length,
+      stft.frameCount,
+      stft.microphoneCount,
+      options.phatRelativeFloor ?? DEFAULT_PHAT_RELATIVE_FLOOR,
+    );
+  }
  return packed;
 }

@@ -445,9 +508,16 @@ export async function computeDasWebGpu(options) {
    maximumGpuComplexMacs = 20_000_000_000,
    maximumFftButterflies = 1_000_000_000,
    stftTransformBatchSize = 32,
+    phatRelativeFloor = DEFAULT_PHAT_RELATIVE_FLOOR,
    gpu,
    powerPreference,
  } = options;
+  const algorithm = requireLocalizationAlgorithmDefinition(
+    experiment.beamforming?.algorithm ?? "das",
+  );
+  if (!isDasAlgorithm(algorithm.id)) {
+    throw new RangeError(`computeDasWebGpu does not implement ${algorithm.id}.`);
+  }
  if (!Array.isArray(channels) || channels.length !== arrayDefinition?.microphones?.length) {
    throw new Error(`채널 수 ${channels?.length ?? 0}와 마이크 수 ${arrayDefinition?.microphones?.length ?? 0}가 다릅니다.`);
  }
@@ -501,7 +571,9 @@ export async function computeDasWebGpu(options) {
        label: "DAS beamforming pipeline",
      }),
    }));
-    const calibration = packMicrophoneCalibration(arrayDefinition.microphones);
+    const calibration = packMicrophoneCalibration(arrayDefinition.microphones, {
+      includeAmplitude: algorithm.spectralWeighting !== "phat",
+    });
    const calibrationBuffer = await withWebGpuErrorScopes(context.device, async () =>
      createBuffer(context.device, {
        label: "DAS microphone calibration",
@@ -543,12 +615,14 @@ export async function computeDasWebGpu(options) {
          }),
      });
      const packedSpectrum = packStftBatch(stft, batchBins, {
+        algorithm: algorithm.id,
        microphones: arrayDefinition.microphones,
        sampleRate,
        fftSize: experiment.stft.fftSize,
        compensateFrequencyResponse:
          experiment.beamforming.compensateFrequencyResponse !== false,
        maxCompensationDb: experiment.beamforming.maxCompensationDb,
+        phatRelativeFloor,
      });
      stftMs += now() - stftStartedAt;
      const binIndices = Uint32Array.from(batchBins);
@@ -688,9 +762,10 @@ export async function computeDasWebGpu(options) {
      completedBins += batchBins.length;
    }

-    const bands = finalizeDasBands(rawMaps, workload, experiment);
+    const bands = finalizeDasBands(rawMaps, workload, experiment, algorithm.rawScale);
    const totalMs = now() - totalStartedAt;
    return {
+      algorithm: algorithm.id,
      backend: "webgpu",
      frameCount: workload.frameCount,
      microphoneCount: workload.microphoneCount,
@@ -12,7 +12,7 @@
 * Flat buffer layouts:
 *   spectrum:    [bin][frame][microphone][real, imaginary]
 *   distances:   [point][microphone]
- *   calibration: [microphone][inverse sensitivity gain, phase offset radians]
+ *   calibration: [microphone][inverse sensitivity gain or unity, phase offset radians]
 *   steering:    [bin][point][microphone][real, imaginary]
 *   output:      [bin][point]
 */
@@ -197,9 +197,13 @@ export function packDasCalibration(calibration) {
  return packed;
 }

-/** Convert array-definition microphone metadata to the shader calibration layout. */
-export function packMicrophoneCalibration(microphones) {
+/**
+ * Convert array-definition microphone metadata to the shader calibration layout.
+ * Phase-only algorithms set includeAmplitude=false while retaining phase offsets.
+ */
+export function packMicrophoneCalibration(microphones, options = {}) {
  if (!Array.isArray(microphones)) throw new TypeError("microphones must be an array.");
+  const includeAmplitude = options.includeAmplitude !== false;
  const packed = new Float32Array(microphones.length * 2);
  for (let index = 0; index < microphones.length; index += 1) {
    const microphone = microphones[index];
@@ -213,7 +217,7 @@ export function packMicrophoneCalibration(microphones) {
    );
    packed[index * 2] = requireFiniteF32(
      `microphones[${index}] inverse sensitivity gain`,
-      10 ** (-sensitivityDb / 20),
+      includeAmplitude ? 10 ** (-sensitivityDb / 20) : 1,
    );
    packed[index * 2 + 1] = requireFiniteF32(
      `microphones[${index}] phase offset`,
@@ -42,7 +42,8 @@
      "type": "object", "additionalProperties": false,
      "required": ["algorithm", "backend", "minimumDistance", "normalization", "dynamicRangeDb"],
      "properties": {
-        "algorithm": { "const": "das" }, "backend": { "enum": ["auto", "cpu", "webgpu"] },
+        "algorithm": { "enum": ["das", "das-phat", "gcc-phat"] },
+        "backend": { "enum": ["auto", "cpu", "webgpu"] },
        "minimumDistance": { "type": "number", "exclusiveMinimum": 0, "maximum": 10 },
        "normalization": { "enum": ["peak", "none"] },
        "dynamicRangeDb": { "type": "number", "minimum": 6, "maximum": 120 },
@@ -15,6 +15,7 @@
    "arrayId": { "type": "string", "minLength": 1 },
    "sceneId": { "type": "string", "minLength": 1 },
    "createdAt": { "type": "string", "format": "date-time" },
+    "algorithm": { "enum": ["das", "das-phat", "gcc-phat"] },
    "backend": { "enum": ["cpu", "webgpu"] },
    "inputHash": { "type": "string", "pattern": "^[a-f0-9]{64}$" },
    "signalStats": {
@@ -43,6 +44,7 @@
      "properties": {
        "total": { "type": "number", "minimum": 0 },
        "signalGeneration": { "type": "number", "minimum": 0 },
+        "localization": { "type": "number", "minimum": 0 },
        "beamforming": { "type": "number", "minimum": 0 }
      }
    },
@@ -64,7 +66,7 @@
      "properties": {
        "rows": { "type": "integer", "minimum": 1, "maximum": 512 },
        "columns": { "type": "integer", "minimum": 1, "maximum": 512 },
-        "scale": { "enum": ["power", "normalized", "db"] },
+        "scale": { "enum": ["power", "score", "normalized", "db"] },
        "values": {
          "type": "array", "minItems": 1, "maxItems": 262144,
          "items": { "type": "number" }
@@ -91,7 +93,12 @@
      ],
      "properties": {
        "calibrated": { "const": false },
-        "method": { "const": "connected-half-power-power-weighted-covariance" },
+        "method": {
+          "enum": [
+            "connected-half-power-power-weighted-covariance",
+            "connected-half-maximum-score-weighted-covariance"
+          ]
+        },
        "halfPowerRatio": { "type": "number", "exclusiveMinimum": 0, "maximum": 1 },
        "thresholdValue": { "type": "number", "minimum": 0 },
        "pixelCount": { "type": "integer", "minimum": 0, "maximum": 262144 },
@@ -11,6 +11,8 @@ globalThis.assertEqual = (actual, expected, message = "values differ") => {
 try {
  await import("/tests/unit/contracts.test.js");
  await import("/tests/unit/acoustics.test.js");
+  await import("/tests/unit/gcc-phat.test.js");
+  await import("/tests/unit/localization.test.js");
  await import("/tests/unit/realtime-localizer.test.js");
  await import("/tests/unit/builder.test.js");
  await import("/tests/unit/evaluation.test.js");
@@ -12,6 +12,13 @@ import {
  prepareDelaySpectrum,
 } from "/packages/acoustics-core/fractional-delay.js";
 import { dbToLinear, distance3 } from "/packages/acoustics-core/math.js";
+import {
+  DEFAULT_LOCALIZATION_ALGORITHM,
+  LOCALIZATION_ALGORITHM_IDS,
+  isDasAlgorithm,
+  localizationAlgorithmDefinition,
+  localizationAlgorithmLabel,
+} from "/packages/acoustics-core/localization-algorithms.js";
 import { renderSceneMix } from "/packages/acoustics-core/mixdown.js";
 import { whiteNoiseAt } from "/packages/acoustics-core/noise.js";
 import { sphericalAttenuation, synthesizeMicrophoneSignals } from "/packages/acoustics-core/propagation.js";
@@ -29,6 +36,115 @@ function assertNear(actual, expected, tolerance, label) {
  assert(Math.abs(actual - expected) <= tolerance, `${label}: expected ${expected} ± ${tolerance}, received ${actual}`);
 }

+test("localization algorithm registry exposes stable ids and capabilities", () => {
+  assertEqual(DEFAULT_LOCALIZATION_ALGORITHM, "das", "default algorithm");
+  assertEqual(
+    LOCALIZATION_ALGORITHM_IDS.join(","),
+    "das,das-phat,gcc-phat",
+    "stable algorithm ids",
+  );
+  assertEqual(localizationAlgorithmDefinition("das").rawScale, "power", "DAS map scale");
+  assertEqual(
+    localizationAlgorithmDefinition("das-phat").spectralWeighting,
+    "phat",
+    "DAS-PHAT weighting",
+  );
+  assertEqual(
+    localizationAlgorithmDefinition("gcc-phat").backends.join(","),
+    "cpu",
+    "GCC-PHAT backend capability",
+  );
+  assert(isDasAlgorithm("das") && isDasAlgorithm("das-phat"), "DAS engine algorithms");
+  assert(!isDasAlgorithm("gcc-phat"), "GCC-PHAT uses a separate engine");
+  assertEqual(localizationAlgorithmLabel("das-phat"), "DAS-PHAT", "algorithm label");
+  assertEqual(localizationAlgorithmDefinition("unknown"), undefined, "unknown lookup");
+});
+
+test("CPU DAS-PHAT is invariant to spectrum and calibration amplitude gains", async () => {
+  const sampleRate = 8_000;
+  const fftSize = 256;
+  const bin = 32;
+  const frequency = (bin * sampleRate) / fftSize;
+  const speedOfSound = 343;
+  const target = [0.2, 0, 1];
+  const positions = [[-0.15, 0, 0], [-0.02, 0, 0], [0.12, 0, 0]];
+  const phaseOffsets = [30, -45, 75];
+  const responsePhases = [10, 20, -35];
+  const createDefinition = (withAmplitudeMetadata) => ({
+    microphones: positions.map((position, index) => ({
+      id: `mic-${index}`,
+      position,
+      sensitivityDb: withAmplitudeMetadata ? [-30, 24, 60][index] : 0,
+      phaseOffsetDeg: phaseOffsets[index],
+      frequencyResponse: [
+        {
+          frequencyHz: 0,
+          gainDb: withAmplitudeMetadata ? [-60, 40, 100][index] : 0,
+          phaseDeg: responsePhases[index],
+        },
+        {
+          frequencyHz: 4_000,
+          gainDb: withAmplitudeMetadata ? [-60, 40, 100][index] : 0,
+          phaseDeg: responsePhases[index],
+        },
+      ],
+    })),
+  });
+  const createSpectrum = (magnitudes) => {
+    const real = new Float32Array(positions.length);
+    const imaginary = new Float32Array(positions.length);
+    positions.forEach((position, index) => {
+      const propagationPhase =
+        (2 * Math.PI * frequency * distance3(target, position)) / speedOfSound;
+      const calibrationPhase =
+        ((phaseOffsets[index] + responsePhases[index]) * Math.PI) / 180;
+      const observedPhase = -propagationPhase + calibrationPhase;
+      real[index] = Math.cos(observedPhase) * magnitudes[index];
+      imaginary[index] = Math.sin(observedPhase) * magnitudes[index];
+    });
+    return { real, imaginary };
+  };
+  const scan = {
+    z: 1,
+    xMin: -0.2,
+    xMax: 0.2,
+    yMin: 0,
+    yMax: 0,
+    columns: 2,
+    rows: 1,
+  };
+  const baselineArray = createDefinition(false);
+  const gainedArray = createDefinition(true);
+  const common = {
+    bin,
+    frameCount: 1,
+    pointCount: 2,
+    sampleRate,
+    fftSize,
+    speedOfSound,
+    algorithm: "das-phat",
+    pointBatchSize: 64,
+  };
+  const baseline = await computeBinMap({
+    ...common,
+    spectrum: createSpectrum([1, 1, 1]),
+    arrayDefinition: baselineArray,
+    distances: createDistanceMatrix(baselineArray, scan),
+  });
+  const gained = await computeBinMap({
+    ...common,
+    spectrum: createSpectrum([0.125, 8, 32]),
+    arrayDefinition: gainedArray,
+    distances: createDistanceMatrix(gainedArray, scan),
+  });
+
+  baseline.forEach((value, index) => {
+    assertNear(gained[index], value, 2e-6, `gain-invariant map value ${index}`);
+  });
+  assertNear(baseline[1], 1, 2e-6, "phase-compensated target score");
+  assert(baseline[1] > baseline[0] * 1.1, "phase compensation preserves target peak");
+});
+
 function rms(values, start = 0, end = values.length) {
  let sum = 0;
  for (let index = start; index < end; index += 1) sum += values[index] ** 2;
@@ -521,12 +637,31 @@ test("CPU DAS peak가 calibration 후 단일 tone의 실제 grid 위치를 찾
    onProgress: (progress) => progressEvents.push(progress),
  });
  const peakPosition = result.bands[0].peak.position;
+  assertEqual(result.algorithm, "das", "default result algorithm");
+  assertEqual(result.bands[0].rawScale, "power", "DAS raw map scale");
  assert(distance3(peakPosition, scene.sources[0].position) <= 0.071, `peak=${peakPosition.join(",")}`);
  assert(result.bands[0].rawMap !== result.bands[0].displayMap, "raw/display buffers are separate");
  assertNear(Math.max(...result.bands[0].rawMap), result.bands[0].peak.rawPower, 1e-4, "raw map peak");
  assertNear(Math.max(...result.bands[0].displayMap), 1, 1e-6, "normalized display peak");
  assert(progressEvents.some(({ stage }) => stage === "beamforming"), "beamforming progress");

+  const phaseOnly = await computeDasMap({
+    channels: synthesis.channels,
+    sampleRate: synthesis.sampleRate,
+    arrayDefinition,
+    scene,
+    experiment: {
+      ...experiment,
+      beamforming: { ...experiment.beamforming, algorithm: "das-phat" },
+    },
+  });
+  assertEqual(phaseOnly.algorithm, "das-phat", "phase-only result algorithm");
+  assertEqual(phaseOnly.bands[0].rawScale, "score", "DAS-PHAT raw map scale");
+  assert(
+    distance3(phaseOnly.bands[0].peak.position, scene.sources[0].position) <= 0.071,
+    `DAS-PHAT peak=${phaseOnly.bands[0].peak.position.join(",")}`,
+  );
+
  const uncompensated = await computeDasMap({
    channels: synthesis.channels,
    sampleRate: synthesis.sampleRate,
@@ -157,6 +157,51 @@ test("단일 tone 장면과 실험이 관계 검증을 통과한다", async () =
  assertEqual((await validateExperiment(experiment, { arrayDefinition, scene })).length, 0, "experiment issues");
 });

+test("세 위치 추정 알고리즘과 backend 제약을 계약이 검증한다", async () => {
+  const [arrayDefinition, scene, baseline] = await Promise.all([
+    loadJson("/assets/array-geometries/rectangular-16x12.array.json"),
+    loadJson("/assets/sample-scenes/single-tone.scene.json"),
+    loadJson("/assets/sample-scenes/single-tone.experiment.json"),
+  ]);
+
+  for (const algorithm of ["das", "das-phat", "gcc-phat"]) {
+    const experiment = structuredClone(baseline);
+    experiment.beamforming.algorithm = algorithm;
+    experiment.beamforming.backend = algorithm === "gcc-phat" ? "cpu" : "auto";
+    assertEqual(
+      (await validateExperiment(experiment, { arrayDefinition, scene })).length,
+      0,
+      `${algorithm} experiment issues`,
+    );
+  }
+
+  const incompatible = structuredClone(baseline);
+  incompatible.beamforming.algorithm = "gcc-phat";
+  incompatible.beamforming.backend = "webgpu";
+  let issues = await validateExperiment(incompatible, { arrayDefinition, scene });
+  assert(
+    issues.some(({ path }) => path === "$.beamforming.backend"),
+    "GCC-PHAT rejects the WebGPU backend",
+  );
+
+  incompatible.beamforming.backend = "cpu";
+  issues = await validateExperiment(incompatible, {
+    arrayDefinition: { ...arrayDefinition, microphones: arrayDefinition.microphones.slice(0, 1) },
+    scene,
+  });
+  assert(
+    issues.some(({ path }) => path === "$.beamforming.algorithm"),
+    "GCC-PHAT requires two microphones",
+  );
+
+  incompatible.beamforming.algorithm = "music";
+  issues = await validateExperiment(incompatible, { arrayDefinition, scene });
+  assert(
+    issues.some(({ path }) => path === "$.beamforming.algorithm"),
+    "unsupported algorithms are rejected",
+  );
+});
+
 test("shoebox 음향 환경의 room과 occluder 경계를 검증한다", async () => {
  const scene = await loadJson("/assets/sample-scenes/single-tone.scene.json");
  scene.acoustics = {
@@ -357,6 +402,7 @@ test("복수 음원 대응 result map 계약과 shape 관계를 검증한다", a
    sceneId: experiment.sceneId,
    createdAt: "2026-08-28T00:00:00.000Z",
    backend: "cpu",
+    algorithm: "das",
    inputHash: "a".repeat(64),
    signalStats: { preNormalizationPeak: 0.5, appliedScale: 1, clippedSampleCount: 0 },
    timingMs: { total: 10, signalGeneration: 3, beamforming: 7 },
@@ -369,10 +415,25 @@ test("복수 음원 대응 result map 계약과 shape 관계를 검증한다", a
    }],
  };
  assertEqual((await validateResult(result, { experiment, scene })).length, 0, "valid result issues");
+
+  experiment.beamforming.algorithm = "das-phat";
+  result.algorithm = "das-phat";
+  result.bands[0].rawMap.scale = "score";
+  assertEqual(
+    (await validateResult(result, { experiment, scene })).length,
+    0,
+    "phase-weighted score result issues",
+  );
+
+  result.algorithm = "das";
+  let issues = await validateResult(result, { experiment, scene });
+  assert(issues.some(({ path }) => path === "$.algorithm"), "result algorithm must match its experiment");
+
+  experiment.beamforming.algorithm = "das";
  result.bands[0].rawMap.values.pop();
  result.bands[0].rawMap.scale = "normalized";
  result.bands[0].peaks[0].sourceId = "missing-source";
-  const issues = await validateResult(result, { experiment, scene });
+  issues = await validateResult(result, { experiment, scene });
  assert(issues.some(({ message }) => message.includes("rows × columns")));
  assert(issues.some(({ path }) => path.endsWith("rawMap.scale")));
  assert(issues.some(({ path }) => path.endsWith("sourceId")));
@@ -1,6 +1,7 @@
 import {
  RESULT_METRIC_KEYS,
  evaluateDasBand,
+  evaluateLocalizationBand,
  exportBandEvaluation,
  findLocalPeaks,
  measureBeamSpread,
@@ -213,6 +214,44 @@ test("beam spread가 회전된 비등방성 half-power lobe의 covariance ellips
  );
 });

+test("generic localization 평가는 score map의 척도와 beam spread 방식을 보존한다", () => {
+  const scan = { xMin: -1, xMax: 1, yMin: -1, yMax: 1, z: 2, columns: 3, rows: 3 };
+  const rawMap = {
+    rows: 3,
+    columns: 3,
+    scale: "score",
+    values: Float32Array.of(
+      0, 1, 0,
+      1, 4, 1,
+      0, 1, 0,
+    ),
+  };
+  const evaluation = evaluateLocalizationBand({
+    rawMap,
+    scan,
+    sources: [{ id: "center", position: [0, 0, 2] }],
+    matchToleranceM: 0,
+    truthRadiusM: 0,
+    minimumPeakDistanceM: 0,
+  });
+
+  assertEqual(evaluation.diagnostics.mapScale, "score", "score scale diagnostic");
+  assertEqual(
+    evaluation.detections[0].beamSpread.method,
+    "connected-half-maximum-score-weighted-covariance",
+    "score beam-spread method",
+  );
+  assertEqual(evaluation.sourceMatches[0].localizationErrorM, 0, "score peak localization");
+
+  const legacy = evaluateDasBand({
+    rawMap: rawMap.values,
+    scan,
+    sources: [],
+    minimumPeakDistanceM: 0,
+  });
+  assertEqual(legacy.diagnostics.mapScale, "power", "legacy DAS defaults to power");
+});
+
 test("beam spread가 1-row, 1-column, single-pixel lobe에서 유한하고 결정적이다", () => {
  const powers = Float32Array.of(5, 8, 10, 8, 5);
  const rowSpread = measureBeamSpread(
@@ -337,6 +376,6 @@ test("evaluation이 non-finite, negative, shape 오류를 입력 경계에서
      scan,
      sources: [],
    })) instanceof RangeError,
-    "non-power map rejected",
+    "display-only map scale rejected",
  );
 });
@@ -0,0 +1,314 @@
+import {
+  computeGccPhatMap,
+  createGccPhatAnalysisPlan,
+  estimateGccPhatWorkload,
+  selectGccPhatReference,
+} from "/packages/acoustics-core/gcc-phat.js";
+import {
+  addFrequencyDomainDelay,
+  createDelayWorkspace,
+  prepareDelaySpectrum,
+} from "/packages/acoustics-core/fractional-delay.js";
+import { distance3 } from "/packages/acoustics-core/math.js";
+
+function assertNear(actual, expected, tolerance, label) {
+  assert(
+    Math.abs(actual - expected) <= tolerance,
+    label + ": expected " + expected + " ± " + tolerance + ", received " + actual,
+  );
+}
+
+function deterministicBroadbandSamples(length) {
+  let state = 0x6d2b79f5;
+  return Float32Array.from({ length }, (_, index) => {
+    state ^= state << 13;
+    state ^= state >>> 17;
+    state ^= state << 5;
+    const noise = ((state >>> 0) / 0xffff_ffff) * 2 - 1;
+    const taper = Math.min(1, index / 64, (length - 1 - index) / 64);
+    return noise * Math.max(0, taper);
+  });
+}
+
+function createBroadbandFixture() {
+  const sampleRate = 4096;
+  const speedOfSound = 343;
+  const target = [0.3, 0.2, 0.6];
+  const arrayDefinition = {
+    id: "gcc-array",
+    microphones: [
+      {
+        id: "a-ref",
+        position: [-0.45, -0.35, 0],
+        sensitivityDb: 9,
+        phaseOffsetDeg: 31,
+        frequencyResponse: [
+          { frequencyHz: 0, gainDb: -8, phaseDeg: 19 },
+          { frequencyHz: sampleRate / 2, gainDb: -8, phaseDeg: 19 },
+        ],
+      },
+      {
+        id: "b-ne",
+        position: [0.45, 0.35, 0],
+        sensitivityDb: -7,
+        phaseOffsetDeg: -47,
+        frequencyResponse: [
+          { frequencyHz: 0, gainDb: 6, phaseDeg: -23 },
+          { frequencyHz: sampleRate / 2, gainDb: 6, phaseDeg: -23 },
+        ],
+      },
+      {
+        id: "c-nw",
+        position: [-0.45, 0.35, 0],
+        sensitivityDb: 4,
+        phaseOffsetDeg: 73,
+      },
+      {
+        id: "d-se",
+        position: [0.45, -0.35, 0],
+        sensitivityDb: -3,
+        phaseOffsetDeg: -11,
+      },
+    ],
+  };
+  const scene = { speedOfSound };
+  const experiment = {
+    stft: { fftSize: 256, hopSize: 128, window: "hann" },
+    scan: {
+      z: target[2],
+      xMin: -0.4,
+      xMax: 0.4,
+      yMin: -0.4,
+      yMax: 0.4,
+      columns: 9,
+      rows: 9,
+    },
+    frequencyBands: [
+      { id: "broadband", minHz: 128, maxHz: 1800, color: "#65f2bf" },
+    ],
+    beamforming: {
+      algorithm: "gcc-phat",
+      backend: "cpu",
+      normalization: "peak",
+      compensateFrequencyResponse: true,
+      maxCompensationDb: 24,
+    },
+  };
+
+  const emitted = deterministicBroadbandSamples(2048);
+  const distances = arrayDefinition.microphones.map((microphone) =>
+    distance3(target, microphone.position)
+  );
+  const maximumDelaySamples = Math.max(...distances) * sampleRate / speedOfSound;
+  const spectrum = prepareDelaySpectrum(emitted, maximumDelaySamples);
+  const workspace = createDelayWorkspace(spectrum.fftSize);
+  const channels = arrayDefinition.microphones.map((microphone, index) => {
+    const channel = new Float32Array(emitted.length);
+    addFrequencyDomainDelay({
+      output: channel,
+      spectrum,
+      delaySamples: distances[index] * sampleRate / speedOfSound,
+      gain: 1 / distances[index],
+      phaseOffsetDeg: microphone.phaseOffsetDeg,
+      frequencyResponse: microphone.frequencyResponse,
+      sampleRate,
+      workspace,
+    });
+    return channel;
+  });
+
+  return {
+    sampleRate,
+    speedOfSound,
+    target,
+    arrayDefinition,
+    scene,
+    experiment,
+    channels,
+  };
+}
+
+function scanIndexFor(scan, x, y) {
+  const column = Math.round((x - scan.xMin) / (scan.xMax - scan.xMin) * (scan.columns - 1));
+  const row = Math.round((scan.yMax - y) / (scan.yMax - scan.yMin) * (scan.rows - 1));
+  return row * scan.columns + column;
+}
+
+function assertFloat32Exact(actual, expected, label) {
+  assertEqual(actual.length, expected.length, label + " length");
+  for (let index = 0; index < actual.length; index += 1) {
+    assertEqual(actual[index], expected[index], label + "[" + index + "]");
+  }
+}
+
+test("GCC-PHAT reference selection uses the farthest centroid radius and id tie-break", () => {
+  const arrayDefinition = {
+    microphones: [
+      { id: "z-left", position: [-1, 0, 0] },
+      { id: "middle", position: [0, 0, 0] },
+      { id: "a-right", position: [1, 0, 0] },
+    ],
+  };
+  const selected = selectGccPhatReference(arrayDefinition);
+  assertEqual(selected.id, "a-right", "lexicographically first id wins a radius tie");
+  assertEqual(selected.index, 2, "selected microphone index");
+  assertEqual(selected.microphone, arrayDefinition.microphones[2], "selected microphone identity");
+});
+
+test("GCC-PHAT workload reports reference-pair scan reduction and rejects aliased lag", () => {
+  const fixture = createBroadbandFixture();
+  const workload = estimateGccPhatWorkload(fixture);
+  assertEqual(workload.pairCount, fixture.arrayDefinition.microphones.length - 1, "reference pair count");
+  assertEqual(
+    workload.scanLookupCount,
+    workload.pointCount * workload.pairCount * workload.bandCount,
+    "one correlation lookup per point, pair, and band",
+  );
+  assert(
+    workload.scanLookupCount < workload.dasEquivalentComplexMacCount,
+    "reference-pair lookup count is below equivalent DAS complex MAC count",
+  );
+  assert(
+    workload.maximumLagSamples > 0 &&
+      workload.maximumLagSamples <= fixture.experiment.stft.fftSize / 2,
+    "maximum geometric lag is exposed and unaliased",
+  );
+
+  let memoryError;
+  try {
+    createGccPhatAnalysisPlan({ ...fixture, maximumPlanBytes: 1 });
+  } catch (error) {
+    memoryError = error;
+  }
+  assert(
+    memoryError instanceof RangeError && memoryError.message.includes("maximumPlanBytes"),
+    "plan hard memory bound",
+  );
+
+  const aliasedArray = {
+    microphones: [
+      { id: "far", position: [-1, 0, 0] },
+      { id: "near", position: [1, 0, 0] },
+    ],
+  };
+  let lagError;
+  try {
+    createGccPhatAnalysisPlan({
+      sampleRate: 48000,
+      arrayDefinition: aliasedArray,
+      scene: { speedOfSound: 343 },
+      experiment: {
+        stft: { fftSize: 128, hopSize: 64, window: "hann" },
+        scan: {
+          z: 0.1,
+          xMin: 0.9,
+          xMax: 0.9,
+          yMin: 0,
+          yMax: 0,
+          columns: 1,
+          rows: 1,
+        },
+        frequencyBands: [{ id: "wide", minHz: 500, maxHz: 5000, color: "#65f2bf" }],
+        beamforming: { algorithm: "gcc-phat", backend: "cpu", normalization: "peak" },
+      },
+    });
+  } catch (error) {
+    lagError = error;
+  }
+  assert(
+    lagError instanceof RangeError &&
+      lagError.message.includes("maximum absolute geometric lag") &&
+      lagError.message.includes("fftSize/2"),
+    "circular-lag alias guard",
+  );
+});
+
+test("GCC-PHAT corrected lag sign finds a deterministic broadband source and emits score maps", async () => {
+  const fixture = createBroadbandFixture();
+  const result = await computeGccPhatMap({
+    ...fixture,
+    phatFloorRatio: 1e-5,
+    pointBatchSize: 1024,
+    stftTransformBatchSize: 1024,
+  });
+  const band = result.bands[0];
+  const peakPosition = band.peak.position;
+  assertEqual(result.backend, "cpu", "CPU backend");
+  assertEqual(result.algorithm, "gcc-phat", "algorithm id");
+  assertEqual(result.referenceMicrophoneId, "a-ref", "deterministic reference metadata");
+  assertEqual(result.pairCount, 3, "pair metadata");
+  assertEqual(result.phatFloorRatio, 1e-5, "PHAT floor metadata");
+  assertNear(result.maximumLagSamples, result.workload.maximumLagSamples, 0, "lag metadata");
+  assertEqual(band.rawScale, "score", "raw map scale");
+  assert(
+    band.rawMap.every((value) => Number.isFinite(value) && value >= 0),
+    "shifted and squared score map is finite and nonnegative",
+  );
+  assert(
+    distance3(peakPosition, fixture.target) <= 0.15,
+    "broadband GCC-PHAT peak is within one grid diagonal: " + peakPosition.join(","),
+  );
+  const targetIndex = scanIndexFor(
+    fixture.experiment.scan,
+    fixture.target[0],
+    fixture.target[1],
+  );
+  const wrongSignIndex = scanIndexFor(
+    fixture.experiment.scan,
+    -fixture.target[0],
+    fixture.target[1],
+  );
+  assert(
+    band.rawMap[targetIndex] > band.rawMap[wrongSignIndex],
+    "X_m * conj(X_ref) correlation uses the physical arrival-lag sign",
+  );
+  assertNear(Math.max(...band.displayMap), 1, 1e-6, "normalized score display");
+});
+
+test("GCC-PHAT analysis plan reuses geometry and FFT workspaces and releases its busy lock", async () => {
+  const fixture = createBroadbandFixture();
+  const plan = createGccPhatAnalysisPlan(fixture);
+  const retainedDistances = plan.distances;
+  const retainedStftWorkspace = plan.stftWorkspace;
+  const retainedCorrelationReal = plan.correlationReal;
+  const computeOptions = {
+    ...fixture,
+    analysisPlan: plan,
+    pointBatchSize: 1024,
+    stftTransformBatchSize: 1024,
+  };
+  const first = await computeGccPhatMap(computeOptions);
+  const second = await computeGccPhatMap(computeOptions);
+
+  assertEqual(plan.distances, retainedDistances, "distance matrix identity is retained");
+  assertEqual(plan.stftWorkspace, retainedStftWorkspace, "STFT workspace identity is retained");
+  assertEqual(plan.correlationReal, retainedCorrelationReal, "correlation workspace identity is retained");
+  assertFloat32Exact(second.bands[0].rawMap, first.bands[0].rawMap, "reused plan raw map");
+  assertEqual(second.analysisCache.analysisAttempts, 2, "analysis attempts");
+  assertEqual(second.analysisCache.completedAnalyses, 2, "completed analyses");
+  assertEqual(second.analysisCache.distanceMatrixBuilds, 1, "distance matrix built once");
+  assertEqual(
+    second.analysisCache.avoidedDistanceEvaluations,
+    plan.pointCount * plan.microphoneCount,
+    "second geometry pass avoided",
+  );
+  assertEqual(second.analysisCache.stftWorkspaceUses, 2, "STFT workspace reused");
+  assertEqual(second.analysisCache.correlationWorkspaceUses, 2, "correlation workspace reused");
+  assertEqual(
+    second.analysisCache.inverseFftTransforms,
+    plan.pairCount * plan.bandBins.length * 2,
+    "one inverse FFT per pair and band per analysis",
+  );
+
+  const controller = new AbortController();
+  controller.abort();
+  let abortError;
+  try {
+    await computeGccPhatMap({ ...computeOptions, signal: controller.signal });
+  } catch (error) {
+    abortError = error;
+  }
+  assertEqual(abortError?.name, "AbortError", "AbortError propagated");
+  assertEqual(plan.busy, false, "plan busy lock released after cancellation");
+  assertEqual(plan.statistics.completedAnalyses, 2, "canceled analysis is not completed");
+});
@@ -0,0 +1,198 @@
+import {
+  computeLocalizationMap,
+  createLocalizationAnalysisPlan,
+  estimateLocalizationWorkload,
+} from "/packages/acoustics-core/localization.js";
+import {
+  DEFAULT_LOCALIZATION_ALGORITHM,
+  LOCALIZATION_ALGORITHM_IDS,
+  localizationAlgorithmDefinition,
+  localizationAlgorithmSupportsBackend,
+} from "/packages/acoustics-core/localization-algorithms.js";
+import {
+  StreamingDasLocalizer,
+  StreamingLocalizer,
+  createLocalizationPlan,
+  detectBandPeaks,
+  localizeMultichannel,
+} from "/packages/acoustics-core/realtime-localizer.js";
+import {
+  computeDasWithBackend,
+  computeLocalizationWithBackend,
+} from "/packages/webgpu-kernels/compute.js";
+
+function createFixture(algorithm = "das") {
+  const sampleRate = 4096;
+  const arrayDefinition = {
+    id: "algorithm-fixture",
+    microphones: [
+      { id: "nw", position: [-0.1, 0.1, 0] },
+      { id: "ne", position: [0.1, 0.1, 0] },
+      { id: "sw", position: [-0.1, -0.1, 0] },
+      { id: "se", position: [0.1, -0.1, 0] },
+    ],
+  };
+  let state = 0x9e3779b9;
+  const emitted = Float32Array.from({ length: 128 }, () => {
+    state ^= state << 13;
+    state ^= state >>> 17;
+    state ^= state << 5;
+    return ((state >>> 0) / 0xffff_ffff) * 2 - 1;
+  });
+  const channels = arrayDefinition.microphones.map((_, microphoneIndex) =>
+    Float32Array.from(
+      { length: emitted.length },
+      (_, sampleIndex) => emitted[Math.max(0, sampleIndex - microphoneIndex)],
+    )
+  );
+  const scan = {
+    z: 1,
+    xMin: -0.2,
+    xMax: 0.2,
+    yMin: -0.2,
+    yMax: 0.2,
+    columns: 3,
+    rows: 3,
+  };
+  const frequencyBands = [
+    { id: "wide", minHz: 128, maxHz: 1800, color: "#65f2bf" },
+  ];
+  const experiment = {
+    stft: { fftSize: 64, hopSize: 32, window: "hann" },
+    scan,
+    frequencyBands,
+    beamforming: {
+      algorithm,
+      backend: "cpu",
+      normalization: "peak",
+      compensateFrequencyResponse: true,
+    },
+  };
+  return {
+    channels,
+    sampleRate,
+    arrayDefinition,
+    scene: { speedOfSound: 343 },
+    experiment,
+  };
+}
+
+test("localization algorithm registry exposes explicit scale and backend semantics", () => {
+  assertEqual(DEFAULT_LOCALIZATION_ALGORITHM, "das", "default algorithm");
+  assertEqual(LOCALIZATION_ALGORITHM_IDS.join(","), "das,das-phat,gcc-phat", "registered ids");
+  assertEqual(localizationAlgorithmDefinition("das").rawScale, "power", "DAS power scale");
+  assertEqual(localizationAlgorithmDefinition("das-phat").rawScale, "score", "DAS-PHAT score scale");
+  assertEqual(localizationAlgorithmDefinition("gcc-phat").rawScale, "score", "GCC-PHAT score scale");
+  assert(localizationAlgorithmSupportsBackend("das-phat", "webgpu"), "DAS-PHAT WebGPU support");
+  assert(!localizationAlgorithmSupportsBackend("gcc-phat", "webgpu"), "GCC-PHAT CPU-only support");
+});
+
+test("generic CPU dispatcher runs all algorithms with explicit raw-map scales", async () => {
+  for (const algorithm of LOCALIZATION_ALGORITHM_IDS) {
+    const fixture = createFixture(algorithm);
+    const analysisPlan = createLocalizationAnalysisPlan(fixture);
+    const workload = estimateLocalizationWorkload({ ...fixture, analysisPlan });
+    const result = await computeLocalizationMap({
+      ...fixture,
+      analysisPlan,
+      pointBatchSize: 1024,
+      stftTransformBatchSize: 1024,
+    });
+    const definition = localizationAlgorithmDefinition(algorithm);
+
+    assertEqual(result.algorithm, algorithm, `${algorithm} result id`);
+    assertEqual(result.backend, "cpu", `${algorithm} backend`);
+    assertEqual(result.bands[0].rawScale, definition.rawScale, `${algorithm} raw scale`);
+    assertEqual(result.bands[0].rawMap.length, 9, `${algorithm} map shape`);
+    assertEqual(result.bands[0].peak.rawScale, definition.rawScale, `${algorithm} peak scale`);
+    assertEqual(
+      "rawPower" in result.bands[0].peak,
+      definition.rawScale === "power",
+      `${algorithm} peak power field`,
+    );
+    assert(
+      result.bands[0].rawMap.every((value) => Number.isFinite(value) && value >= 0),
+      `${algorithm} finite nonnegative map`,
+    );
+    assert(workload.pointCount === 9 && workload.microphoneCount === 4, `${algorithm} workload`);
+  }
+});
+
+test("backend dispatcher keeps GCC-PHAT on CPU and rejects an explicit GPU request", async () => {
+  const fixture = createFixture("gcc-phat");
+  const calls = [];
+  const result = await computeLocalizationWithBackend(fixture, {
+    computeGpu: async () => {
+      calls.push("gpu");
+      throw new Error("GCC-PHAT must not call WebGPU");
+    },
+    computeGccCpu: async () => {
+      calls.push("gcc-cpu");
+      return { backend: "cpu", algorithm: "gcc-phat", bands: [] };
+    },
+  });
+  assertEqual(calls.join(","), "gcc-cpu", "GCC auto routing");
+  assertEqual(result.algorithm, "gcc-phat", "GCC result");
+
+  fixture.experiment.beamforming.backend = "webgpu";
+  let backendError;
+  try {
+    await computeLocalizationWithBackend(fixture);
+  } catch (error) {
+    backendError = error;
+  }
+  assert(backendError instanceof TypeError, "explicit GCC WebGPU is rejected");
+
+  let legacyError;
+  try {
+    await computeDasWithBackend(fixture);
+  } catch (error) {
+    legacyError = error;
+  }
+  assert(legacyError instanceof RangeError, "legacy DAS entry rejects GCC");
+});
+
+test("portable and streaming API accept an algorithm choice without Scene synthesis", async () => {
+  const fixture = createFixture("gcc-phat");
+  const config = {
+    sampleRate: fixture.sampleRate,
+    array: fixture.arrayDefinition,
+    scan: fixture.experiment.scan,
+    bands: fixture.experiment.frequencyBands,
+    speedOfSound: fixture.scene.speedOfSound,
+    stft: fixture.experiment.stft,
+    algorithm: "gcc-phat",
+    phatFloorRatio: 1e-5,
+    normalization: "peak",
+    computeOptions: { pointBatchSize: 1024, stftTransformBatchSize: 1024 },
+  };
+  const plan = createLocalizationPlan(config);
+  const localized = await localizeMultichannel({ plan, channels: fixture.channels });
+  assertEqual(plan.algorithm, "gcc-phat", "portable plan algorithm");
+  assertEqual(plan.computeOptions.phatFloorRatio, 1e-5, "portable PHAT floor forwarding");
+  assertEqual(plan.analysisPlan, plan.gccPhatPlan, "portable GCC plan alias");
+  assertEqual(localized.algorithm, "gcc-phat", "portable result algorithm");
+  assertEqual(localized.phatFloorRatio, 1e-5, "portable PHAT floor reaches GCC core");
+  assertEqual(localized.bands[0].rawScale, "score", "portable score map");
+  assertEqual(StreamingLocalizer, StreamingDasLocalizer, "streaming compatibility alias");
+
+  const stream = new StreamingLocalizer({
+    ...config,
+    analysisWindowSamples: 128,
+    analysisHopSamples: 128,
+    maximumBufferedSamples: 256,
+  });
+  const streamed = await stream.push(fixture.channels);
+  assertEqual(streamed.length, 1, "one GCC streaming result");
+  assertEqual(streamed[0].algorithm, "gcc-phat", "streaming result algorithm");
+  assertEqual(stream.getState().algorithm, "gcc-phat", "streaming state algorithm");
+  assertEqual(stream.plan.gccPhatPlan.busy, false, "streaming GCC plan lock released");
+
+  const scorePeaks = detectBandPeaks(
+    Float32Array.of(0, 2, 0, 1),
+    { z: 1, xMin: 0, xMax: 1, yMin: 0, yMax: 1, columns: 2, rows: 2 },
+    { rawScale: "score", minimumPeakDistanceM: 0 },
+  );
+  assertEqual(scorePeaks[0].rawScale, "score", "portable peak scale");
+  assert(!("rawPower" in scorePeaks[0]), "score is not mislabeled as acoustic power");
+});
@@ -12,6 +12,48 @@ import {
 } from "/packages/webgpu-kernels/das.js";
 import { computeDasWithBackend } from "/packages/webgpu-kernels/compute.js";

+test("WebGPU DAS-PHAT packing phase-normalizes above a relative floor", () => {
+  const stft = {
+    frameCount: 1,
+    microphoneCount: 3,
+    spectra: new Map([[
+      8,
+      {
+        real: Float32Array.of(3, 0, 1e-8),
+        imaginary: Float32Array.of(4, 2, 0),
+      },
+    ]]),
+  };
+  const microphones = [
+    {
+      frequencyResponse: [
+        { frequencyHz: 0, gainDb: 60, phaseDeg: 90 },
+        { frequencyHz: 4_000, gainDb: 60, phaseDeg: 90 },
+      ],
+    },
+    {
+      frequencyResponse: [
+        { frequencyHz: 0, gainDb: -60, phaseDeg: 0 },
+        { frequencyHz: 4_000, gainDb: -60, phaseDeg: 0 },
+      ],
+    },
+    {},
+  ];
+  const packed = packStftBatch(stft, [8], {
+    algorithm: "das-phat",
+    microphones,
+    sampleRate: 8_000,
+    fftSize: 256,
+  });
+
+  assertNear(packed[0], 0.8, 1e-6, "response-phase corrected real");
+  assertNear(packed[1], -0.6, 1e-6, "response-phase corrected imaginary");
+  assertNear(packed[2], 0, 1e-7, "second microphone real");
+  assertNear(packed[3], 1, 1e-7, "response gain is ignored");
+  assertEqual(packed[4], 0, "below-floor real is suppressed");
+  assertEqual(packed[5], 0, "below-floor imaginary is suppressed");
+});
+
 function assertNear(actual, expected, tolerance, label) {
  assert(
    Math.abs(actual - expected) <= tolerance,
@@ -53,6 +95,17 @@ test("WebGPU DAS uniform과 microphone calibration을 WGSL layout으로 packing
  assertNear(calibration[3], Math.PI / 2, 1e-6, "+90 degree phase");
  assertNear(calibration[4], 2, 1e-6, "-6 dB inverse gain");
  assertNear(calibration[5], -Math.PI / 4, 1e-6, "-45 degree phase");
+
+  const phaseOnlyCalibration = packMicrophoneCalibration([
+    { sensitivityDb: 0, phaseOffsetDeg: 0 },
+    { sensitivityDb: 6.020599913, phaseOffsetDeg: 90 },
+    { sensitivityDb: -6.020599913, phaseOffsetDeg: -45 },
+  ], { includeAmplitude: false });
+  assertNear(phaseOnlyCalibration[0], 1, 1e-7, "phase-only unity gain 0");
+  assertNear(phaseOnlyCalibration[2], 1, 1e-7, "phase-only unity gain 1");
+  assertNear(phaseOnlyCalibration[4], 1, 1e-7, "phase-only unity gain 2");
+  assertNear(phaseOnlyCalibration[3], Math.PI / 2, 1e-6, "phase-only preserves +90 phase");
+  assertNear(phaseOnlyCalibration[5], -Math.PI / 4, 1e-6, "phase-only preserves -45 phase");
 });

 test("STFT batch를 bin-frame-microphone-complex 순서로 packing한다", () => {