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* Move 'VadWithLevel' to AGC2 where it belongs. * Remove the vectors from VadWithLevel. They were there to make it work with modules/audio_processing/vad, which we don't need any longer. * Remove the vector handling from AGC2. It was spread out across AdaptiveDigitalGainApplier, AdaptiveAGC and their unit tests. * Hack the RNN VAD into VadWithLevel. The main issue is the resampling. Bug: webrtc:9076 Change-Id: I13056c985d0ec41269735150caf4aaeb6ff9281e Reviewed-on: https://webrtc-review.googlesource.com/77364 Reviewed-by: Sam Zackrisson <saza@webrtc.org> Commit-Queue: Alex Loiko <aleloi@webrtc.org> Cr-Commit-Position: refs/heads/master@{#23688}
68 lines
2.3 KiB
C++
68 lines
2.3 KiB
C++
/*
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* Copyright (c) 2018 The WebRTC project authors. All Rights Reserved.
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*
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* Use of this source code is governed by a BSD-style license
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* that can be found in the LICENSE file in the root of the source
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* tree. An additional intellectual property rights grant can be found
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* in the file PATENTS. All contributing project authors may
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* be found in the AUTHORS file in the root of the source tree.
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*/
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#include "modules/audio_processing/agc2/vad_with_level.h"
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#include <algorithm>
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#include "common_audio/include/audio_util.h"
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#include "modules/audio_processing/agc2/rnn_vad/common.h"
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#include "rtc_base/checks.h"
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namespace webrtc {
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namespace {
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float ProcessForPeak(AudioFrameView<const float> frame) {
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float current_max = 0;
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for (const auto& x : frame.channel(0)) {
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current_max = std::max(std::fabs(x), current_max);
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}
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return current_max;
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}
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float ProcessForRms(AudioFrameView<const float> frame) {
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float rms = 0;
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for (const auto& x : frame.channel(0)) {
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rms += x * x;
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}
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return sqrt(rms / frame.samples_per_channel());
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}
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} // namespace
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VadWithLevel::VadWithLevel() = default;
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VadWithLevel::~VadWithLevel() = default;
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VadWithLevel::LevelAndProbability VadWithLevel::AnalyzeFrame(
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AudioFrameView<const float> frame) {
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SetSampleRate(static_cast<int>(frame.samples_per_channel() * 100));
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std::array<float, rnn_vad::kFrameSize10ms24kHz> work_frame;
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// Feed the 1st channel to the resampler.
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resampler_.Resample(frame.channel(0).data(), frame.samples_per_channel(),
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work_frame.data(), rnn_vad::kFrameSize10ms24kHz);
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std::array<float, rnn_vad::kFeatureVectorSize> feature_vector;
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const bool is_silence = features_extractor_.CheckSilenceComputeFeatures(
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work_frame, feature_vector);
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const float vad_probability =
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rnn_vad_.ComputeVadProbability(feature_vector, is_silence);
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return LevelAndProbability(vad_probability,
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FloatS16ToDbfs(ProcessForRms(frame)),
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FloatS16ToDbfs(ProcessForPeak(frame)));
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}
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void VadWithLevel::SetSampleRate(int sample_rate_hz) {
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// The source number of channels in 1, because we always use the 1st
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// channel.
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resampler_.InitializeIfNeeded(sample_rate_hz, rnn_vad::kSampleRate24kHz,
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1 /* num_channels */);
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}
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} // namespace webrtc
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