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VAD Refactoring: Repalced WebRtc_ types with stdint

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BUG=
TEST=vad_unittests,audioproc_unittest

Review URL: https://webrtc-codereview.appspot.com/460009

git-svn-id: http://webrtc.googlecode.com/svn/trunk@1954 4adac7df-926f-26a2-2b94-8c16560cd09d
This commit is contained in:
bjornv@webrtc.org 2012-03-29 12:09:44 +00:00
parent d713143d99
commit ac2ea030fc
4 changed files with 105 additions and 109 deletions
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6
src/common_audio/vad/include/webrtc_vad.h
View File

@ -96,10 +96,8 @@ int WebRtcVad_set_mode(VadInst* handle, int mode);
* 0 - Non-active Voice
* -1 - Error
*/
WebRtc_Word16 WebRtcVad_Process(VadInst *vad_inst,
WebRtc_Word16 fs,
WebRtc_Word16 *speech_frame,
WebRtc_Word16 frame_length);
int16_t WebRtcVad_Process(VadInst* vad_inst, int16_t fs, int16_t* speech_frame,
int16_t frame_length);
#ifdef __cplusplus
}

150
src/common_audio/vad/vad_core.c
View File

@ -17,40 +17,40 @@
#include "vad_sp.h"
// Spectrum Weighting
static const WebRtc_Word16 kSpectrumWeight[6] = { 6, 8, 10, 12, 14, 16 };
static const WebRtc_Word16 kNoiseUpdateConst = 655; // Q15
static const WebRtc_Word16 kSpeechUpdateConst = 6554; // Q15
static const WebRtc_Word16 kBackEta = 154; // Q8
static const int16_t kSpectrumWeight[6] = { 6, 8, 10, 12, 14, 16 };
static const int16_t kNoiseUpdateConst = 655; // Q15
static const int16_t kSpeechUpdateConst = 6554; // Q15
static const int16_t kBackEta = 154; // Q8
// Minimum difference between the two models, Q5
static const WebRtc_Word16 kMinimumDifference[6] = {
static const int16_t kMinimumDifference[6] = {
544, 544, 576, 576, 576, 576 };
// Upper limit of mean value for speech model, Q7
static const WebRtc_Word16 kMaximumSpeech[6] = {
static const int16_t kMaximumSpeech[6] = {
11392, 11392, 11520, 11520, 11520, 11520 };
// Minimum value for mean value
static const WebRtc_Word16 kMinimumMean[2] = { 640, 768 };
static const int16_t kMinimumMean[2] = { 640, 768 };
// Upper limit of mean value for noise model, Q7
static const WebRtc_Word16 kMaximumNoise[6] = {
static const int16_t kMaximumNoise[6] = {
9216, 9088, 8960, 8832, 8704, 8576 };
// Start values for the Gaussian models, Q7
// Weights for the two Gaussians for the six channels (noise)
static const WebRtc_Word16 kNoiseDataWeights[12] = {
static const int16_t kNoiseDataWeights[12] = {
34, 62, 72, 66, 53, 25, 94, 66, 56, 62, 75, 103 };
// Weights for the two Gaussians for the six channels (speech)
static const WebRtc_Word16 kSpeechDataWeights[12] = {
static const int16_t kSpeechDataWeights[12] = {
48, 82, 45, 87, 50, 47, 80, 46, 83, 41, 78, 81 };
// Means for the two Gaussians for the six channels (noise)
static const WebRtc_Word16 kNoiseDataMeans[12] = {
static const int16_t kNoiseDataMeans[12] = {
6738, 4892, 7065, 6715, 6771, 3369, 7646, 3863, 7820, 7266, 5020, 4362 };
// Means for the two Gaussians for the six channels (speech)
static const WebRtc_Word16 kSpeechDataMeans[12] = {
static const int16_t kSpeechDataMeans[12] = {
8306, 10085, 10078, 11823, 11843, 6309, 9473, 9571, 10879, 7581, 8180, 7483
};
// Stds for the two Gaussians for the six channels (noise)
static const WebRtc_Word16 kNoiseDataStds[12] = {
static const int16_t kNoiseDataStds[12] = {
378, 1064, 493, 582, 688, 593, 474, 697, 475, 688, 421, 455 };
// Stds for the two Gaussians for the six channels (speech)
static const WebRtc_Word16 kSpeechDataStds[12] = {
static const int16_t kSpeechDataStds[12] = {
555, 505, 567, 524, 585, 1231, 509, 828, 492, 1540, 1079, 850 };
// Constants used in GmmProbability().
@ -100,30 +100,30 @@ static const int16_t kGlobalThresholdVAG[3] = { 1100, 1050, 1100 };
// - frame_length [i] : Number of input samples
//
// - returns : the VAD decision (0 - noise, 1 - speech).
static int16_t GmmProbability(VadInstT *inst, WebRtc_Word16 *feature_vector,
WebRtc_Word16 total_power, int frame_length)
static int16_t GmmProbability(VadInstT *inst, int16_t *feature_vector,
int16_t total_power, int frame_length)
{
int n, k;
WebRtc_Word16 backval;
WebRtc_Word16 h0, h1;
WebRtc_Word16 ratvec, xval;
WebRtc_Word16 vadflag;
WebRtc_Word16 shifts0, shifts1;
WebRtc_Word16 tmp16, tmp16_1, tmp16_2;
WebRtc_Word16 diff, nr, pos;
WebRtc_Word16 nmk, nmk2, nmk3, smk, smk2, nsk, ssk;
WebRtc_Word16 delt, ndelt;
WebRtc_Word16 maxspe, maxmu;
WebRtc_Word16 deltaN[kTableSize], deltaS[kTableSize];
WebRtc_Word16 ngprvec[kTableSize], sgprvec[kTableSize];
WebRtc_Word32 h0test, h1test;
WebRtc_Word32 tmp32_1, tmp32_2;
WebRtc_Word32 dotVal;
WebRtc_Word32 nmid, smid;
WebRtc_Word32 probn[kNumGaussians], probs[kNumGaussians];
WebRtc_Word16 *nmean1ptr, *nmean2ptr, *smean1ptr, *smean2ptr, *nstd1ptr, *nstd2ptr,
int16_t backval;
int16_t h0, h1;
int16_t ratvec, xval;
int16_t vadflag;
int16_t shifts0, shifts1;
int16_t tmp16, tmp16_1, tmp16_2;
int16_t diff, nr, pos;
int16_t nmk, nmk2, nmk3, smk, smk2, nsk, ssk;
int16_t delt, ndelt;
int16_t maxspe, maxmu;
int16_t deltaN[kTableSize], deltaS[kTableSize];
int16_t ngprvec[kTableSize], sgprvec[kTableSize];
int32_t h0test, h1test;
int32_t tmp32_1, tmp32_2;
int32_t dotVal;
int32_t nmid, smid;
int32_t probn[kNumGaussians], probs[kNumGaussians];
int16_t *nmean1ptr, *nmean2ptr, *smean1ptr, *smean2ptr, *nstd1ptr, *nstd2ptr,
*sstd1ptr, *sstd2ptr;
WebRtc_Word16 overhead1, overhead2, individualTest, totalTest;
int16_t overhead1, overhead2, individualTest, totalTest;
// Set the thresholds to different values based on frame length
if (frame_length == 80)
@ -173,22 +173,22 @@ static int16_t GmmProbability(VadInstT *inst, WebRtc_Word16 *feature_vector,
// Probability for Noise, Q7 * Q20 = Q27
tmp32_1 = WebRtcVad_GaussianProbability(xval, *nmean1ptr++, *nstd1ptr++,
&deltaN[pos]);
probn[0] = (WebRtc_Word32)(kNoiseDataWeights[n] * tmp32_1);
probn[0] = (int32_t)(kNoiseDataWeights[n] * tmp32_1);
tmp32_1 = WebRtcVad_GaussianProbability(xval, *nmean2ptr++, *nstd2ptr++,
&deltaN[pos + 1]);
probn[1] = (WebRtc_Word32)(kNoiseDataWeights[n + kNumChannels] * tmp32_1);
probn[1] = (int32_t)(kNoiseDataWeights[n + kNumChannels] * tmp32_1);
h0test = probn[0] + probn[1]; // Q27
h0 = (WebRtc_Word16)WEBRTC_SPL_RSHIFT_W32(h0test, 12); // Q15
h0 = (int16_t)WEBRTC_SPL_RSHIFT_W32(h0test, 12); // Q15
// Probability for Speech
tmp32_1 = WebRtcVad_GaussianProbability(xval, *smean1ptr++, *sstd1ptr++,
&deltaS[pos]);
probs[0] = (WebRtc_Word32)(kSpeechDataWeights[n] * tmp32_1);
probs[0] = (int32_t)(kSpeechDataWeights[n] * tmp32_1);
tmp32_1 = WebRtcVad_GaussianProbability(xval, *smean2ptr++, *sstd2ptr++,
&deltaS[pos + 1]);
probs[1] = (WebRtc_Word32)(kSpeechDataWeights[n + kNumChannels] * tmp32_1);
probs[1] = (int32_t)(kSpeechDataWeights[n + kNumChannels] * tmp32_1);
h1test = probs[0] + probs[1]; // Q27
h1 = (WebRtc_Word16)WEBRTC_SPL_RSHIFT_W32(h1test, 12); // Q15
h1 = (int16_t)WEBRTC_SPL_RSHIFT_W32(h1test, 12); // Q15
// Get likelihood ratio. Approximate log2(H1/H0) with shifts0 - shifts1
shifts0 = WebRtcSpl_NormW32(h0test);
@ -222,7 +222,7 @@ static int16_t GmmProbability(VadInstT *inst, WebRtc_Word16 *feature_vector,
{
tmp32_1 = probn[0] & 0xFFFFF000; // Q27
tmp32_2 = WEBRTC_SPL_LSHIFT_W32(tmp32_1, 2); // Q29
ngprvec[pos] = (WebRtc_Word16)WebRtcSpl_DivW32W16(tmp32_2, h0);
ngprvec[pos] = (int16_t)WebRtcSpl_DivW32W16(tmp32_2, h0);
ngprvec[pos + 1] = 16384 - ngprvec[pos];
} else
{
@ -235,7 +235,7 @@ static int16_t GmmProbability(VadInstT *inst, WebRtc_Word16 *feature_vector,
{
tmp32_1 = probs[0] & 0xFFFFF000;
tmp32_2 = WEBRTC_SPL_LSHIFT_W32(tmp32_1, 2);
sgprvec[pos] = (WebRtc_Word16)WebRtcSpl_DivW32W16(tmp32_2, h1);
sgprvec[pos] = (int16_t)WebRtcSpl_DivW32W16(tmp32_2, h1);
sgprvec[pos + 1] = 16384 - sgprvec[pos];
} else
{
@ -271,7 +271,7 @@ static int16_t GmmProbability(VadInstT *inst, WebRtc_Word16 *feature_vector,
nmid = WEBRTC_SPL_MUL_16_16(kNoiseDataWeights[n], *nmean1ptr); // Q7 * Q7
nmid += WEBRTC_SPL_MUL_16_16(kNoiseDataWeights[n+kNumChannels],
*(nmean1ptr+kNumChannels));
tmp16_1 = (WebRtc_Word16)WEBRTC_SPL_RSHIFT_W32(nmid, 6); // Q8
tmp16_1 = (int16_t)WEBRTC_SPL_RSHIFT_W32(nmid, 6); // Q8
for (k = 0; k < kNumGaussians; k++)
{
@ -294,9 +294,9 @@ static int16_t GmmProbability(VadInstT *inst, WebRtc_Word16 *feature_vector,
// deltaN = (x-mu)/sigma^2
// ngprvec[k] = probn[k]/(probn[0] + probn[1])
delt = (WebRtc_Word16)WEBRTC_SPL_MUL_16_16_RSFT(ngprvec[nr],
delt = (int16_t)WEBRTC_SPL_MUL_16_16_RSFT(ngprvec[nr],
deltaN[nr], 11); // Q14*Q11
nmk2 = nmk + (WebRtc_Word16)WEBRTC_SPL_MUL_16_16_RSFT(delt,
nmk2 = nmk + (int16_t)WEBRTC_SPL_MUL_16_16_RSFT(delt,
kNoiseUpdateConst,
22); // Q7+(Q14*Q15>>22)
}
@ -304,7 +304,7 @@ static int16_t GmmProbability(VadInstT *inst, WebRtc_Word16 *feature_vector,
// Long term correction of the noise mean
ndelt = WEBRTC_SPL_LSHIFT_W16(backval, 4);
ndelt -= tmp16_1; // Q8 - Q8
nmk3 = nmk2 + (WebRtc_Word16)WEBRTC_SPL_MUL_16_16_RSFT(ndelt,
nmk3 = nmk2 + (int16_t)WEBRTC_SPL_MUL_16_16_RSFT(ndelt,
kBackEta,
9); // Q7+(Q8*Q8)>>9
@ -323,10 +323,10 @@ static int16_t GmmProbability(VadInstT *inst, WebRtc_Word16 *feature_vector,
// deltaS = (x-mu)/sigma^2
// sgprvec[k] = probn[k]/(probn[0] + probn[1])
delt = (WebRtc_Word16)WEBRTC_SPL_MUL_16_16_RSFT(sgprvec[nr],
delt = (int16_t)WEBRTC_SPL_MUL_16_16_RSFT(sgprvec[nr],
deltaS[nr],
11); // (Q14*Q11)>>11=Q14
tmp16 = (WebRtc_Word16)WEBRTC_SPL_MUL_16_16_RSFT(delt,
tmp16 = (int16_t)WEBRTC_SPL_MUL_16_16_RSFT(delt,
kSpeechUpdateConst,
21) + 1;
smk2 = smk + (tmp16 >> 1); // Q7 + (Q14 * Q15 >> 22)
@ -345,18 +345,18 @@ static int16_t GmmProbability(VadInstT *inst, WebRtc_Word16 *feature_vector,
tmp16 = feature_vector[n] - tmp16; // Q4
tmp32_1 = WEBRTC_SPL_MUL_16_16_RSFT(deltaS[nr], tmp16, 3);
tmp32_2 = tmp32_1 - (WebRtc_Word32)4096; // Q12
tmp32_2 = tmp32_1 - (int32_t)4096; // Q12
tmp16 = WEBRTC_SPL_RSHIFT_W16((sgprvec[nr]), 2);
tmp32_1 = (WebRtc_Word32)(tmp16 * tmp32_2);// (Q15>>3)*(Q14>>2)=Q12*Q12=Q24
tmp32_1 = (int32_t)(tmp16 * tmp32_2);// (Q15>>3)*(Q14>>2)=Q12*Q12=Q24
tmp32_2 = WEBRTC_SPL_RSHIFT_W32(tmp32_1, 4); // Q20
// 0.1 * Q20 / Q7 = Q13
if (tmp32_2 > 0)
tmp16 = (WebRtc_Word16)WebRtcSpl_DivW32W16(tmp32_2, ssk * 10);
tmp16 = (int16_t)WebRtcSpl_DivW32W16(tmp32_2, ssk * 10);
else
{
tmp16 = (WebRtc_Word16)WebRtcSpl_DivW32W16(-tmp32_2, ssk * 10);
tmp16 = (int16_t)WebRtcSpl_DivW32W16(-tmp32_2, ssk * 10);
tmp16 = -tmp16;
}
// divide by 4 giving an update factor of 0.025
@ -375,17 +375,17 @@ static int16_t GmmProbability(VadInstT *inst, WebRtc_Word16 *feature_vector,
// (Q15>>3) * (Q14>>2) = Q12 * Q12 = Q24
tmp32_1 = WEBRTC_SPL_MUL_16_16_RSFT(deltaN[nr], tmp16, 3) - 4096;
tmp16 = WEBRTC_SPL_RSHIFT_W16((ngprvec[nr]+2), 2);
tmp32_2 = (WebRtc_Word32)(tmp16 * tmp32_1);
tmp32_2 = (int32_t)(tmp16 * tmp32_1);
tmp32_1 = WEBRTC_SPL_RSHIFT_W32(tmp32_2, 14);
// Q20 * approx 0.001 (2^-10=0.0009766)
// Q20 / Q7 = Q13
tmp16 = (WebRtc_Word16)WebRtcSpl_DivW32W16(tmp32_1, nsk);
tmp16 = (int16_t)WebRtcSpl_DivW32W16(tmp32_1, nsk);
if (tmp32_1 > 0)
tmp16 = (WebRtc_Word16)WebRtcSpl_DivW32W16(tmp32_1, nsk);
tmp16 = (int16_t)WebRtcSpl_DivW32W16(tmp32_1, nsk);
else
{
tmp16 = (WebRtc_Word16)WebRtcSpl_DivW32W16(-tmp32_1, nsk);
tmp16 = (int16_t)WebRtcSpl_DivW32W16(-tmp32_1, nsk);
tmp16 = -tmp16;
}
tmp16 += 32; // Rounding
@ -407,8 +407,8 @@ static int16_t GmmProbability(VadInstT *inst, WebRtc_Word16 *feature_vector,
smid += WEBRTC_SPL_MUL_16_16(kSpeechDataWeights[n+kNumChannels], *smean2ptr);
// diff = "global" speech mean - "global" noise mean
diff = (WebRtc_Word16)WEBRTC_SPL_RSHIFT_W32(smid, 9);
tmp16 = (WebRtc_Word16)WEBRTC_SPL_RSHIFT_W32(nmid, 9);
diff = (int16_t)WEBRTC_SPL_RSHIFT_W32(smid, 9);
tmp16 = (int16_t)WEBRTC_SPL_RSHIFT_W32(nmid, 9);
diff -= tmp16;
if (diff < kMinimumDifference[n])
@ -418,8 +418,8 @@ static int16_t GmmProbability(VadInstT *inst, WebRtc_Word16 *feature_vector,
// tmp16_1 = ~0.8 * (kMinimumDifference - diff) in Q7
// tmp16_2 = ~0.2 * (kMinimumDifference - diff) in Q7
tmp16_1 = (WebRtc_Word16)WEBRTC_SPL_MUL_16_16_RSFT(13, tmp16, 2);
tmp16_2 = (WebRtc_Word16)WEBRTC_SPL_MUL_16_16_RSFT(3, tmp16, 2);
tmp16_1 = (int16_t)WEBRTC_SPL_MUL_16_16_RSFT(13, tmp16, 2);
tmp16_2 = (int16_t)WEBRTC_SPL_MUL_16_16_RSFT(3, tmp16, 2);
// First Gauss, speech model
tmp16 = tmp16_1 + *smean1ptr;
@ -445,7 +445,7 @@ static int16_t GmmProbability(VadInstT *inst, WebRtc_Word16 *feature_vector,
// Control that the speech & noise means do not drift to much
maxspe = kMaximumSpeech[n];
tmp16_2 = (WebRtc_Word16)WEBRTC_SPL_RSHIFT_W32(smid, 7);
tmp16_2 = (int16_t)WEBRTC_SPL_RSHIFT_W32(smid, 7);
if (tmp16_2 > maxspe)
{ // Upper limit of speech model
tmp16_2 -= maxspe;
@ -454,7 +454,7 @@ static int16_t GmmProbability(VadInstT *inst, WebRtc_Word16 *feature_vector,
*smean2ptr -= tmp16_2;
}
tmp16_2 = (WebRtc_Word16)WEBRTC_SPL_RSHIFT_W32(nmid, 7);
tmp16_2 = (int16_t)WEBRTC_SPL_RSHIFT_W32(nmid, 7);
if (tmp16_2 > kMaximumNoise[n])
{
tmp16_2 -= kMaximumNoise[n];
@ -610,12 +610,12 @@ int WebRtcVad_set_mode_core(VadInstT* self, int mode) {
// Calculate VAD decision by first extracting feature values and then calculate
// probability for both speech and background noise.
WebRtc_Word16 WebRtcVad_CalcVad32khz(VadInstT *inst, WebRtc_Word16 *speech_frame,
int frame_length)
int16_t WebRtcVad_CalcVad32khz(VadInstT* inst, int16_t* speech_frame,
int frame_length)
{
WebRtc_Word16 len, vad;
WebRtc_Word16 speechWB[480]; // Downsampled speech frame: 960 samples (30ms in SWB)
WebRtc_Word16 speechNB[240]; // Downsampled speech frame: 480 samples (30ms in WB)
int16_t len, vad;
int16_t speechWB[480]; // Downsampled speech frame: 960 samples (30ms in SWB)
int16_t speechNB[240]; // Downsampled speech frame: 480 samples (30ms in WB)
// Downsample signal 32->16->8 before doing VAD
@ -632,11 +632,11 @@ WebRtc_Word16 WebRtcVad_CalcVad32khz(VadInstT *inst, WebRtc_Word16 *speech_frame
return vad;
}
WebRtc_Word16 WebRtcVad_CalcVad16khz(VadInstT *inst, WebRtc_Word16 *speech_frame,
int frame_length)
int16_t WebRtcVad_CalcVad16khz(VadInstT* inst, int16_t* speech_frame,
int frame_length)
{
WebRtc_Word16 len, vad;
WebRtc_Word16 speechNB[240]; // Downsampled speech frame: 480 samples (30ms in WB)
int16_t len, vad;
int16_t speechNB[240]; // Downsampled speech frame: 480 samples (30ms in WB)
// Wideband: Downsample signal before doing VAD
WebRtcVad_Downsampling(speech_frame, speechNB, inst->downsampling_filter_states,
@ -648,10 +648,10 @@ WebRtc_Word16 WebRtcVad_CalcVad16khz(VadInstT *inst, WebRtc_Word16 *speech_frame
return vad;
}
WebRtc_Word16 WebRtcVad_CalcVad8khz(VadInstT *inst, WebRtc_Word16 *speech_frame,
int frame_length)
int16_t WebRtcVad_CalcVad8khz(VadInstT* inst, int16_t* speech_frame,
int frame_length)
{
WebRtc_Word16 feature_vector[kNumChannels], total_power;
int16_t feature_vector[kNumChannels], total_power;
// Get power in the bands
total_power = WebRtcVad_CalculateFeatures(inst, speech_frame, frame_length,

50
src/common_audio/vad/vad_core.h
View File

@ -26,28 +26,28 @@ enum { kMinEnergy = 10 }; // Minimum energy required to trigger audio signal.
typedef struct VadInstT_
{
WebRtc_Word16 vad;
WebRtc_Word32 downsampling_filter_states[4];
WebRtc_Word16 noise_means[kTableSize];
WebRtc_Word16 speech_means[kTableSize];
WebRtc_Word16 noise_stds[kTableSize];
WebRtc_Word16 speech_stds[kTableSize];
int16_t vad;
int32_t downsampling_filter_states[4];
int16_t noise_means[kTableSize];
int16_t speech_means[kTableSize];
int16_t noise_stds[kTableSize];
int16_t speech_stds[kTableSize];
// TODO(bjornv): Change to |frame_count|.
WebRtc_Word32 frame_counter;
WebRtc_Word16 over_hang; // Over Hang
WebRtc_Word16 num_of_speech;
int32_t frame_counter;
int16_t over_hang; // Over Hang
int16_t num_of_speech;
// TODO(bjornv): Change to |age_vector|.
WebRtc_Word16 index_vector[16 * kNumChannels];
WebRtc_Word16 low_value_vector[16 * kNumChannels];
int16_t index_vector[16 * kNumChannels];
int16_t low_value_vector[16 * kNumChannels];
// TODO(bjornv): Change to |median|.
WebRtc_Word16 mean_value[kNumChannels];
WebRtc_Word16 upper_state[5];
WebRtc_Word16 lower_state[5];
WebRtc_Word16 hp_filter_state[4];
WebRtc_Word16 over_hang_max_1[3];
WebRtc_Word16 over_hang_max_2[3];
WebRtc_Word16 individual[3];
WebRtc_Word16 total[3];
int16_t mean_value[kNumChannels];
int16_t upper_state[5];
int16_t lower_state[5];
int16_t hp_filter_state[4];
int16_t over_hang_max_1[3];
int16_t over_hang_max_2[3];
int16_t individual[3];
int16_t total[3];
int init_flag;
@ -100,11 +100,11 @@ int WebRtcVad_set_mode_core(VadInstT* self, int mode);
* 0 - No active speech
* 1-6 - Active speech
*/
WebRtc_Word16 WebRtcVad_CalcVad32khz(VadInstT* inst, WebRtc_Word16* speech_frame,
int frame_length);
WebRtc_Word16 WebRtcVad_CalcVad16khz(VadInstT* inst, WebRtc_Word16* speech_frame,
int frame_length);
WebRtc_Word16 WebRtcVad_CalcVad8khz(VadInstT* inst, WebRtc_Word16* speech_frame,
int frame_length);
int16_t WebRtcVad_CalcVad32khz(VadInstT* inst, int16_t* speech_frame,
int frame_length);
int16_t WebRtcVad_CalcVad16khz(VadInstT* inst, int16_t* speech_frame,
int frame_length);
int16_t WebRtcVad_CalcVad8khz(VadInstT* inst, int16_t* speech_frame,
int frame_length);
#endif // WEBRTC_COMMON_AUDIO_VAD_VAD_CORE_H_

8
src/common_audio/vad/webrtc_vad.c
View File

@ -79,12 +79,10 @@ int WebRtcVad_set_mode(VadInst* handle, int mode) {
return WebRtcVad_set_mode_core(self, mode);
}
WebRtc_Word16 WebRtcVad_Process(VadInst *vad_inst,
WebRtc_Word16 fs,
WebRtc_Word16 *speech_frame,
WebRtc_Word16 frame_length)
int16_t WebRtcVad_Process(VadInst* vad_inst, int16_t fs, int16_t* speech_frame,
int16_t frame_length)
{
WebRtc_Word16 vad;
int16_t vad;
VadInstT* vad_ptr;
if (vad_inst == NULL)