| /* |
| * 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 "modules/audio_processing/vad/voice_activity_detector.h" |
| |
| #include <algorithm> |
| #include <vector> |
| |
| #include "test/gtest.h" |
| #include "test/testsupport/file_utils.h" |
| |
| namespace webrtc { |
| namespace { |
| |
| const int kStartTimeSec = 16; |
| const float kMeanSpeechProbability = 0.3f; |
| const float kMaxNoiseProbability = 0.1f; |
| 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 |