AnotherFoxGuy/webrtc-audio-processing
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Bump to WebRTC M120 release

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Some API deprecation -- ExperimentalAgc and ExperimentalNs are gone.
We're continuing to carry iSAC even though it's gone upstream, but maybe
we'll want to drop that soon.
This commit is contained in:
Arun Raghavan
2023-12-12 10:42:58 -05:00
parent 9a202fb8c2
commit c6abf6cd3f
479 changed files with 20900 additions and 11996 deletions
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46
webrtc/common_audio/vad/vad_core.c
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@ -90,11 +90,11 @@ static const int16_t kOverHangMax2VAG[3] = { 9, 5, 3 };
static const int16_t kLocalThresholdVAG[3] = { 94, 94, 94 };
static const int16_t kGlobalThresholdVAG[3] = { 1100, 1050, 1100 };
// Calculates the weighted average w.r.t. number of Gaussians. The |data| are
// updated with an |offset| before averaging.
// Calculates the weighted average w.r.t. number of Gaussians. The `data` are
// updated with an `offset` before averaging.
//
// - data [i/o] : Data to average.
// - offset [i] : An offset added to |data|.
// - offset [i] : An offset added to `data`.
// - weights [i] : Weights used for averaging.
//
// returns : The weighted average.
@ -124,7 +124,7 @@ static inline int32_t RTC_NO_SANITIZE("signed-integer-overflow")
// type of signal is most probable.
//
// - self [i/o] : Pointer to VAD instance
// - features [i] : Feature vector of length |kNumChannels|
// - features [i] : Feature vector of length `kNumChannels`
// = log10(energy in frequency band)
// - total_power [i] : Total power in audio frame.
// - frame_length [i] : Number of input samples
@ -183,10 +183,10 @@ static int16_t GmmProbability(VadInstT* self, int16_t* features,
// H1: Speech
//
// We combine a global LRT with local tests, for each frequency sub-band,
// here defined as |channel|.
// here defined as `channel`.
for (channel = 0; channel < kNumChannels; channel++) {
// For each channel we model the probability with a GMM consisting of
// |kNumGaussians|, with different means and standard deviations depending
// `kNumGaussians`, with different means and standard deviations depending
// on H0 or H1.
h0_test = 0;
h1_test = 0;
@ -234,7 +234,7 @@ static int16_t GmmProbability(VadInstT* self, int16_t* features,
}
log_likelihood_ratio = shifts_h0 - shifts_h1;
// Update |sum_log_likelihood_ratios| with spectrum weighting. This is
// Update `sum_log_likelihood_ratios` with spectrum weighting. This is
// used for the global VAD decision.
sum_log_likelihood_ratios +=
(int32_t) (log_likelihood_ratio * kSpectrumWeight[channel]);
@ -298,8 +298,8 @@ static int16_t GmmProbability(VadInstT* self, int16_t* features,
nmk2 = nmk;
if (!vadflag) {
// deltaN = (x-mu)/sigma^2
// ngprvec[k] = |noise_probability[k]| /
// (|noise_probability[0]| + |noise_probability[1]|)
// ngprvec[k] = `noise_probability[k]` /
// (`noise_probability[0]` + `noise_probability[1]`)
// (Q14 * Q11 >> 11) = Q14.
delt = (int16_t)((ngprvec[gaussian] * deltaN[gaussian]) >> 11);
@ -326,9 +326,9 @@ static int16_t GmmProbability(VadInstT* self, int16_t* features,
if (vadflag) {
// Update speech mean vector:
// |deltaS| = (x-mu)/sigma^2
// sgprvec[k] = |speech_probability[k]| /
// (|speech_probability[0]| + |speech_probability[1]|)
// `deltaS` = (x-mu)/sigma^2
// sgprvec[k] = `speech_probability[k]` /
// (`speech_probability[0]` + `speech_probability[1]`)
// (Q14 * Q11) >> 11 = Q14.
delt = (int16_t)((sgprvec[gaussian] * deltaS[gaussian]) >> 11);
@ -409,35 +409,35 @@ static int16_t GmmProbability(VadInstT* self, int16_t* features,
}
// Separate models if they are too close.
// |noise_global_mean| in Q14 (= Q7 * Q7).
// `noise_global_mean` in Q14 (= Q7 * Q7).
noise_global_mean = WeightedAverage(&self->noise_means[channel], 0,
&kNoiseDataWeights[channel]);
// |speech_global_mean| in Q14 (= Q7 * Q7).
// `speech_global_mean` in Q14 (= Q7 * Q7).
speech_global_mean = WeightedAverage(&self->speech_means[channel], 0,
&kSpeechDataWeights[channel]);
// |diff| = "global" speech mean - "global" noise mean.
// `diff` = "global" speech mean - "global" noise mean.
// (Q14 >> 9) - (Q14 >> 9) = Q5.
diff = (int16_t) (speech_global_mean >> 9) -
(int16_t) (noise_global_mean >> 9);
if (diff < kMinimumDifference[channel]) {
tmp_s16 = kMinimumDifference[channel] - diff;
// |tmp1_s16| = ~0.8 * (kMinimumDifference - diff) in Q7.
// |tmp2_s16| = ~0.2 * (kMinimumDifference - diff) in Q7.
// `tmp1_s16` = ~0.8 * (kMinimumDifference - diff) in Q7.
// `tmp2_s16` = ~0.2 * (kMinimumDifference - diff) in Q7.
tmp1_s16 = (int16_t)((13 * tmp_s16) >> 2);
tmp2_s16 = (int16_t)((3 * tmp_s16) >> 2);
// Move Gaussian means for speech model by |tmp1_s16| and update
// |speech_global_mean|. Note that |self->speech_means[channel]| is
// Move Gaussian means for speech model by `tmp1_s16` and update
// `speech_global_mean`. Note that `self->speech_means[channel]` is
// changed after the call.
speech_global_mean = WeightedAverage(&self->speech_means[channel],
tmp1_s16,
&kSpeechDataWeights[channel]);
// Move Gaussian means for noise model by -|tmp2_s16| and update
// |noise_global_mean|. Note that |self->noise_means[channel]| is
// Move Gaussian means for noise model by -`tmp2_s16` and update
// `noise_global_mean`. Note that `self->noise_means[channel]` is
// changed after the call.
noise_global_mean = WeightedAverage(&self->noise_means[channel],
-tmp2_s16,
@ -534,7 +534,7 @@ int WebRtcVad_InitCore(VadInstT* self) {
self->mean_value[i] = 1600;
}
// Set aggressiveness mode to default (=|kDefaultMode|).
// Set aggressiveness mode to default (=`kDefaultMode`).
if (WebRtcVad_set_mode_core(self, kDefaultMode) != 0) {
return -1;
}
@ -609,7 +609,7 @@ int WebRtcVad_CalcVad48khz(VadInstT* inst, const int16_t* speech_frame,
int vad;
size_t i;
int16_t speech_nb[240]; // 30 ms in 8 kHz.
// |tmp_mem| is a temporary memory used by resample function, length is
// `tmp_mem` is a temporary memory used by resample function, length is
// frame length in 10 ms (480 samples) + 256 extra.
int32_t tmp_mem[480 + 256] = { 0 };
const size_t kFrameLen10ms48khz = 480;