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diff --git a/testing/web-platform/tests/webaudio/resources/distance-model-testing.js b/testing/web-platform/tests/webaudio/resources/distance-model-testing.js
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+++ b/testing/web-platform/tests/webaudio/resources/distance-model-testing.js
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+// Use a power of two to eliminate round-off when converting frames to time and
+// vice versa.
+let sampleRate = 32768;
+
+// How many panner nodes to create for the test.
+let nodesToCreate = 100;
+
+// Time step when each panner node starts.  Make sure it starts on a frame
+// boundary.
+let timeStep = Math.floor(0.001 * sampleRate) / sampleRate;
+
+// Make sure we render long enough to get all of our nodes.
+let renderLengthSeconds = timeStep * (nodesToCreate + 1);
+
+// Length of an impulse signal.
+let pulseLengthFrames = Math.round(timeStep * sampleRate);
+
+// Globals to make debugging a little easier.
+let context;
+let impulse;
+let bufferSource;
+let panner;
+let position;
+let time;
+
+// For the record, these distance formulas were taken from the OpenAL
+// spec
+// (http://connect.creativelabs.com/openal/Documentation/OpenAL%201.1%20Specification.pdf),
+// not the code.  The Web Audio spec follows the OpenAL formulas.
+
+function linearDistance(panner, x, y, z) {
+  let distance = Math.sqrt(x * x + y * y + z * z);
+  distance = Math.min(distance, panner.maxDistance);
+  let rolloff = panner.rolloffFactor;
+  let gain =
+      (1 -
+       rolloff * (distance - panner.refDistance) /
+           (panner.maxDistance - panner.refDistance));
+
+  return gain;
+}
+
+function inverseDistance(panner, x, y, z) {
+  let distance = Math.sqrt(x * x + y * y + z * z);
+  distance = Math.min(distance, panner.maxDistance);
+  let rolloff = panner.rolloffFactor;
+  let gain = panner.refDistance /
+      (panner.refDistance + rolloff * (distance - panner.refDistance));
+
+  return gain;
+}
+
+function exponentialDistance(panner, x, y, z) {
+  let distance = Math.sqrt(x * x + y * y + z * z);
+  distance = Math.min(distance, panner.maxDistance);
+  let rolloff = panner.rolloffFactor;
+  let gain = Math.pow(distance / panner.refDistance, -rolloff);
+
+  return gain;
+}
+
+// Map the distance model to the function that implements the model
+let distanceModelFunction = {
+  'linear': linearDistance,
+  'inverse': inverseDistance,
+  'exponential': exponentialDistance
+};
+
+function createGraph(context, distanceModel, nodeCount) {
+  bufferSource = new Array(nodeCount);
+  panner = new Array(nodeCount);
+  position = new Array(nodeCount);
+  time = new Array(nodesToCreate);
+
+  impulse = createImpulseBuffer(context, pulseLengthFrames);
+
+  // Create all the sources and panners.
+  //
+  // We MUST use the EQUALPOWER panning model so that we can easily
+  // figure out the gain introduced by the panner.
+  //
+  // We want to stay in the middle of the panning range, which means
+  // we want to stay on the z-axis.  If we don't, then the effect of
+  // panning model will be much more complicated.  We're not testing
+  // the panner, but the distance model, so we want the panner effect
+  // to be simple.
+  //
+  // The panners are placed at a uniform intervals between the panner
+  // reference distance and the panner max distance.  The source is
+  // also started at regular intervals.
+  for (let k = 0; k < nodeCount; ++k) {
+    bufferSource[k] = context.createBufferSource();
+    bufferSource[k].buffer = impulse;
+
+    panner[k] = context.createPanner();
+    panner[k].panningModel = 'equalpower';
+    panner[k].distanceModel = distanceModel;
+
+    let distanceStep =
+        (panner[k].maxDistance - panner[k].refDistance) / nodeCount;
+    position[k] = distanceStep * k + panner[k].refDistance;
+    panner[k].setPosition(0, 0, position[k]);
+
+    bufferSource[k].connect(panner[k]);
+    panner[k].connect(context.destination);
+
+    time[k] = k * timeStep;
+    bufferSource[k].start(time[k]);
+  }
+}
+
+// distanceModel should be the distance model string like
+// "linear", "inverse", or "exponential".
+function createTestAndRun(context, distanceModel, should) {
+  // To test the distance models, we create a number of panners at
+  // uniformly spaced intervals on the z-axis.  Each of these are
+  // started at equally spaced time intervals.  After rendering the
+  // signals, we examine where each impulse is located and the
+  // attenuation of the impulse.  The attenuation is compared
+  // against our expected attenuation.
+
+  createGraph(context, distanceModel, nodesToCreate);
+
+  return context.startRendering().then(
+      buffer => checkDistanceResult(buffer, distanceModel, should));
+}
+
+// The gain caused by the EQUALPOWER panning model, if we stay on the
+// z axis, with the default orientations.
+function equalPowerGain() {
+  return Math.SQRT1_2;
+}
+
+function checkDistanceResult(renderedBuffer, model, should) {
+  renderedData = renderedBuffer.getChannelData(0);
+
+  // The max allowed error between the actual gain and the expected
+  // value.  This is determined experimentally.  Set to 0 to see
+  // what the actual errors are.
+  let maxAllowedError = 2.2720e-6;
+
+  let success = true;
+
+  // Number of impulses we found in the rendered result.
+  let impulseCount = 0;
+
+  // Maximum relative error in the gain of the impulses.
+  let maxError = 0;
+
+  // Array of locations of the impulses that were not at the
+  // expected location.  (Contains the actual and expected frame
+  // of the impulse.)
+  let impulsePositionErrors = new Array();
+
+  // Step through the rendered data to find all the non-zero points
+  // so we can find where our distance-attenuated impulses are.
+  // These are tested against the expected attenuations at that
+  // distance.
+  for (let k = 0; k < renderedData.length; ++k) {
+    if (renderedData[k] != 0) {
+      // Convert from string to index.
+      let distanceFunction = distanceModelFunction[model];
+      let expected =
+          distanceFunction(panner[impulseCount], 0, 0, position[impulseCount]);
+
+      // Adjust for the center-panning of the EQUALPOWER panning
+      // model that we're using.
+      expected *= equalPowerGain();
+
+      let error = Math.abs(renderedData[k] - expected) / Math.abs(expected);
+
+      maxError = Math.max(maxError, Math.abs(error));
+
+      should(renderedData[k]).beCloseTo(expected, {threshold: maxAllowedError});
+
+      // Keep track of any impulses that aren't where we expect them
+      // to be.
+      let expectedOffset = timeToSampleFrame(time[impulseCount], sampleRate);
+      if (k != expectedOffset) {
+        impulsePositionErrors.push({actual: k, expected: expectedOffset});
+      }
+      ++impulseCount;
+    }
+  }
+  should(impulseCount, 'Number of impulses').beEqualTo(nodesToCreate);
+
+  should(maxError, 'Max error in distance gains')
+      .beLessThanOrEqualTo(maxAllowedError);
+
+  // Display any timing errors that we found.
+  if (impulsePositionErrors.length > 0) {
+    let actual = impulsePositionErrors.map(x => x.actual);
+    let expected = impulsePositionErrors.map(x => x.expected);
+    should(actual, 'Actual impulse positions found').beEqualToArray(expected);
+  }
+}