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diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/RecognizeCommands.java b/tensorflow/examples/android/src/org/tensorflow/demo/RecognizeCommands.java
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+/*
+ * Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *       http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.tensorflow.demo;
+
+import android.util.Log;
+import android.util.Pair;
+import java.util.ArrayDeque;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Deque;
+import java.util.List;
+
+/** Reads in results from an instantaneous audio recognition model and smoothes them over time. */
+public class RecognizeCommands {
+  // Configuration settings.
+  private List<String> labels = new ArrayList<String>();
+  private long averageWindowDurationMs;
+  private float detectionThreshold;
+  private int suppressionMs;
+  private int minimumCount;
+  private long minimumTimeBetweenSamplesMs;
+
+  // Working variables.
+  private Deque<Pair<Long, float[]>> previousResults = new ArrayDeque<Pair<Long, float[]>>();
+  private String previousTopLabel;
+  private int labelsCount;
+  private long previousTopLabelTime;
+  private float previousTopLabelScore;
+
+  private static final String SILENCE_LABEL = "_silence_";
+  private static final long MINIMUM_TIME_FRACTION = 4;
+
+  public RecognizeCommands(
+      List<String> inLabels,
+      long inAverageWindowDurationMs,
+      float inDetectionThreshold,
+      int inSuppressionMS,
+      int inMinimumCount,
+      long inMinimumTimeBetweenSamplesMS) {
+    labels = inLabels;
+    averageWindowDurationMs = inAverageWindowDurationMs;
+    detectionThreshold = inDetectionThreshold;
+    suppressionMs = inSuppressionMS;
+    minimumCount = inMinimumCount;
+    labelsCount = inLabels.size();
+    previousTopLabel = SILENCE_LABEL;
+    previousTopLabelTime = Long.MIN_VALUE;
+    previousTopLabelScore = 0.0f;
+    minimumTimeBetweenSamplesMs = inMinimumTimeBetweenSamplesMS;
+  }
+
+  /** Holds information about what's been recognized. */
+  public static class RecognitionResult {
+    public final String foundCommand;
+    public final float score;
+    public final boolean isNewCommand;
+
+    public RecognitionResult(String inFoundCommand, float inScore, boolean inIsNewCommand) {
+      foundCommand = inFoundCommand;
+      score = inScore;
+      isNewCommand = inIsNewCommand;
+    }
+  }
+
+  private static class ScoreForSorting implements Comparable<ScoreForSorting> {
+    public final float score;
+    public final int index;
+
+    public ScoreForSorting(float inScore, int inIndex) {
+      score = inScore;
+      index = inIndex;
+    }
+
+    @Override
+    public int compareTo(ScoreForSorting other) {
+      if (this.score > other.score) {
+        return -1;
+      } else if (this.score < other.score) {
+        return 1;
+      } else {
+        return 0;
+      }
+    }
+  }
+
+  public RecognitionResult processLatestResults(float[] currentResults, long currentTimeMS) {
+    if (currentResults.length != labelsCount) {
+      throw new RuntimeException(
+          "The results for recognition should contain "
+              + labelsCount
+              + " elements, but there are "
+              + currentResults.length);
+    }
+
+    if ((!previousResults.isEmpty()) && (currentTimeMS < previousResults.getFirst().first)) {
+      throw new RuntimeException(
+          "You must feed results in increasing time order, but received a timestamp of "
+              + currentTimeMS
+              + " that was earlier than the previous one of "
+              + previousResults.getFirst().first);
+    }
+
+    final int howManyResults = previousResults.size();
+    // Ignore any results that are coming in too frequently.
+    if (howManyResults > 1) {
+      final long timeSinceMostRecent = currentTimeMS - previousResults.getLast().first;
+      if (timeSinceMostRecent < minimumTimeBetweenSamplesMs) {
+        return new RecognitionResult(previousTopLabel, previousTopLabelScore, false);
+      }
+    }
+
+    // Add the latest results to the head of the queue.
+    previousResults.addLast(new Pair<Long, float[]>(currentTimeMS, currentResults));
+
+    // Prune any earlier results that are too old for the averaging window.
+    final long timeLimit = currentTimeMS - averageWindowDurationMs;
+    while (previousResults.getFirst().first < timeLimit) {
+      previousResults.removeFirst();
+    }
+
+    // If there are too few results, assume the result will be unreliable and
+    // bail.
+    final long earliestTime = previousResults.getFirst().first;
+    final long samplesDuration = currentTimeMS - earliestTime;
+    if ((howManyResults < minimumCount)
+        || (samplesDuration < (averageWindowDurationMs / MINIMUM_TIME_FRACTION))) {
+      Log.v("RecognizeResult", "Too few results");
+      return new RecognitionResult(previousTopLabel, 0.0f, false);
+    }
+
+    // Calculate the average score across all the results in the window.
+    float[] averageScores = new float[labelsCount];
+    for (Pair<Long, float[]> previousResult : previousResults) {
+      final float[] scoresTensor = previousResult.second;
+      int i = 0;
+      while (i < scoresTensor.length) {
+        averageScores[i] += scoresTensor[i] / howManyResults;
+        ++i;
+      }
+    }
+
+    // Sort the averaged results in descending score order.
+    ScoreForSorting[] sortedAverageScores = new ScoreForSorting[labelsCount];
+    for (int i = 0; i < labelsCount; ++i) {
+      sortedAverageScores[i] = new ScoreForSorting(averageScores[i], i);
+    }
+    Arrays.sort(sortedAverageScores);
+
+    // See if the latest top score is enough to trigger a detection.
+    final int currentTopIndex = sortedAverageScores[0].index;
+    final String currentTopLabel = labels.get(currentTopIndex);
+    final float currentTopScore = sortedAverageScores[0].score;
+    // If we've recently had another label trigger, assume one that occurs too
+    // soon afterwards is a bad result.
+    long timeSinceLastTop;
+    if (previousTopLabel.equals(SILENCE_LABEL) || (previousTopLabelTime == Long.MIN_VALUE)) {
+      timeSinceLastTop = Long.MAX_VALUE;
+    } else {
+      timeSinceLastTop = currentTimeMS - previousTopLabelTime;
+    }
+    boolean isNewCommand;
+    if ((currentTopScore > detectionThreshold) && (timeSinceLastTop > suppressionMs)) {
+      previousTopLabel = currentTopLabel;
+      previousTopLabelTime = currentTimeMS;
+      previousTopLabelScore = currentTopScore;
+      isNewCommand = true;
+    } else {
+      isNewCommand = false;
+    }
+    return new RecognitionResult(currentTopLabel, currentTopScore, isNewCommand);
+  }
+}