Pull the Voice Activity Detector out from the AGC

webrtc/modules/audio_processing/vad/voice_activity_detector_unittest.cc

+/*
+ *  Copyright (c) 2015 The WebRTC project authors. All Rights Reserved.
+ *
+ *  Use of this source code is governed by a BSD-style license
+ *  that can be found in the LICENSE file in the root of the source
+ *  tree. An additional intellectual property rights grant can be found
+ *  in the file PATENTS.  All contributing project authors may
+ *  be found in the AUTHORS file in the root of the source tree.
+ */
+
+#include "webrtc/modules/audio_processing/vad/voice_activity_detector.h"
+
+#include <algorithm>
+#include <vector>
+
+#include "testing/gtest/include/gtest/gtest.h"
+#include "webrtc/test/testsupport/fileutils.h"
+
+namespace webrtc {
+namespace {
+
+const int kStartTimeSec = 16;
+const float kMeanSpeechProbability = 0.3f;
+const float kMaxNoiseProbability = 0.07f;
+const size_t kNumChunks = 300u;
+const size_t kNumChunksPerIsacBlock = 3;
+
+void GenerateNoise(std::vector<int16_t>* data) {
+  for (size_t i = 0; i < data->size(); ++i) {
+    // std::rand returns between 0 and RAND_MAX, but this will work because it
+    // wraps into some random place.
+    (*data)[i] = std::rand();
+  }
+}
+
+}  // namespace
+
+TEST(VoiceActivityDetectorTest, ConstructorSetsDefaultValues) {
+  const float kDefaultVoiceValue = 1.f;
+
+  VoiceActivityDetector vad;
+
+  std::vector<double> p = vad.chunkwise_voice_probabilities();
+  std::vector<double> rms = vad.chunkwise_rms();
+
+  EXPECT_EQ(p.size(), 0u);
+  EXPECT_EQ(rms.size(), 0u);
+
+  EXPECT_FLOAT_EQ(vad.last_voice_probability(), kDefaultVoiceValue);
+}
+
+TEST(VoiceActivityDetectorTest, Speech16kHzHasHighVoiceProbabilities) {
+  const int kSampleRateHz = 16000;
+  const int kLength10Ms = kSampleRateHz / 100;
+
+  VoiceActivityDetector vad;
+
+  std::vector<int16_t> data(kLength10Ms);
+  float mean_probability = 0.f;
+
+  FILE* pcm_file =
+      fopen(test::ResourcePath("audio_processing/transient/audio16kHz", "pcm")
+                .c_str(),
+            "rb");
+  ASSERT_TRUE(pcm_file != nullptr);
+  // The silences in the file are skipped to get a more robust voice probability
+  // for speech.
+  ASSERT_EQ(fseek(pcm_file, kStartTimeSec * kSampleRateHz * sizeof(data[0]),
+                  SEEK_SET),
+            0);
+
+  size_t num_chunks = 0;
+  while (fread(&data[0], sizeof(data[0]), data.size(), pcm_file) ==
+         data.size()) {
+    vad.ProcessChunk(&data[0], data.size(), kSampleRateHz);
+
+    mean_probability += vad.last_voice_probability();
+
+    ++num_chunks;
+  }
+
+  mean_probability /= num_chunks;
+
+  EXPECT_GT(mean_probability, kMeanSpeechProbability);
+}
+
+TEST(VoiceActivityDetectorTest, Speech32kHzHasHighVoiceProbabilities) {
+  const int kSampleRateHz = 32000;
+  const int kLength10Ms = kSampleRateHz / 100;
+
+  VoiceActivityDetector vad;
+
+  std::vector<int16_t> data(kLength10Ms);
+  float mean_probability = 0.f;
+
+  FILE* pcm_file =
+      fopen(test::ResourcePath("audio_processing/transient/audio32kHz", "pcm")
+                .c_str(),
+            "rb");
+  ASSERT_TRUE(pcm_file != nullptr);
+  // The silences in the file are skipped to get a more robust voice probability
+  // for speech.
+  ASSERT_EQ(fseek(pcm_file, kStartTimeSec * kSampleRateHz * sizeof(data[0]),
+                  SEEK_SET),
+            0);
+
+  size_t num_chunks = 0;
+  while (fread(&data[0], sizeof(data[0]), data.size(), pcm_file) ==
+         data.size()) {
+    vad.ProcessChunk(&data[0], data.size(), kSampleRateHz);
+
+    mean_probability += vad.last_voice_probability();
+
+    ++num_chunks;
+  }
+
+  mean_probability /= num_chunks;
+
+  EXPECT_GT(mean_probability, kMeanSpeechProbability);
+}
+
+TEST(VoiceActivityDetectorTest, Noise16kHzHasLowVoiceProbabilities) {
+  VoiceActivityDetector vad;
+
+  std::vector<int16_t> data(kLength10Ms);
+  float max_probability = 0.f;
+
+  std::srand(42);
+
+  for (size_t i = 0; i < kNumChunks; ++i) {
+    GenerateNoise(&data);
+
+    vad.ProcessChunk(&data[0], data.size(), kSampleRateHz);
+
+    // Before the |vad has enough data to process an ISAC block it will return
+    // the default value, 1.f, which would ruin the |max_probability| value.
+    if (i > kNumChunksPerIsacBlock) {
+      max_probability = std::max(max_probability, vad.last_voice_probability());
+    }
+  }
+
+  EXPECT_LT(max_probability, kMaxNoiseProbability);
+}
+
+TEST(VoiceActivityDetectorTest, Noise32kHzHasLowVoiceProbabilities) {
+  VoiceActivityDetector vad;
+
+  std::vector<int16_t> data(2 * kLength10Ms);
+  float max_probability = 0.f;
+
+  std::srand(42);
+
+  for (size_t i = 0; i < kNumChunks; ++i) {
+    GenerateNoise(&data);
+
+    vad.ProcessChunk(&data[0], data.size(), 2 * kSampleRateHz);
+
+    // Before the |vad has enough data to process an ISAC block it will return
+    // the default value, 1.f, which would ruin the |max_probability| value.
+    if (i > kNumChunksPerIsacBlock) {
+      max_probability = std::max(max_probability, vad.last_voice_probability());
+    }
+  }
+
+  EXPECT_LT(max_probability, kMaxNoiseProbability);
+}
+
+}  // namespace webrtc