Refactoring vad_gmm.[c/h].
- Changed to stdint. - Replaced SHIFT macros. - Variable name changes. - Style changes. - Comments updates. - Added a unit test. Review URL: http://webrtc-codereview.appspot.com/323011 git-svn-id: http://webrtc.googlecode.com/svn/trunk@1249 4adac7df-926f-26a2-2b94-8c16560cd09d
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bjornv@webrtc.org
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@ -8,68 +8,76 @@
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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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/*
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* This file includes the implementation of the internal VAD call
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* WebRtcVad_GaussianProbability. For function description, see vad_gmm.h.
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*/
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#include "vad_gmm.h"
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#include "signal_processing_library.h"
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#include "typedefs.h"
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static const WebRtc_Word32 kCompVar = 22005;
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// Constant log2(exp(1)) in Q12
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static const WebRtc_Word16 kLog10Const = 5909;
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static const int32_t kCompVar = 22005;
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static const int16_t kLog2Exp = 5909; // log2(exp(1)) in Q12.
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WebRtc_Word32 WebRtcVad_GaussianProbability(WebRtc_Word16 in_sample,
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WebRtc_Word16 mean,
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WebRtc_Word16 std,
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WebRtc_Word16 *delta)
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{
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WebRtc_Word16 tmp16, tmpDiv, tmpDiv2, expVal, tmp16_1, tmp16_2;
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WebRtc_Word32 tmp32, y32;
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// For a normal distribution, the probability of |input| is calculated and
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// returned (in Q20). The formula for normal distributed probability is
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//
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// 1 / s * exp(-(x - m)^2 / (2 * s^2))
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//
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// where the parameters are given in the following Q domains:
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// m = |mean| (Q7)
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// s = |std| (Q7)
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// x = |input| (Q4)
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// in addition to the probability we output |delta| (in Q11) used when updating
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// the noise/speech model.
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int32_t WebRtcVad_GaussianProbability(int16_t input,
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int16_t mean,
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int16_t std,
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int16_t* delta) {
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int16_t tmp16, inv_std, inv_std2, exp_value = 0;
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int32_t tmp32;
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// Calculate tmpDiv=1/std, in Q10
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tmp32 = (WebRtc_Word32)WEBRTC_SPL_RSHIFT_W16(std,1) + (WebRtc_Word32)131072; // 1 in Q17
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tmpDiv = (WebRtc_Word16)WebRtcSpl_DivW32W16(tmp32, std); // Q17/Q7 = Q10
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// Calculate |inv_std| = 1 / s, in Q10.
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// 131072 = 1 in Q17, and (|std| >> 1) is for rounding instead of truncation.
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// Q-domain: Q17 / Q7 = Q10.
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tmp32 = (int32_t) 131072 + (int32_t) (std >> 1);
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inv_std = (int16_t) WebRtcSpl_DivW32W16(tmp32, std);
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// Calculate tmpDiv2=1/std^2, in Q14
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tmp16 = WEBRTC_SPL_RSHIFT_W16(tmpDiv, 2); // From Q10 to Q8
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tmpDiv2 = (WebRtc_Word16)WEBRTC_SPL_MUL_16_16_RSFT(tmp16, tmp16, 2); // (Q8 * Q8)>>2 = Q14
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// Calculate |inv_std2| = 1 / s^2, in Q14.
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tmp16 = (inv_std >> 2); // Q10 -> Q8.
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// Q-domain: (Q8 * Q8) >> 2 = Q14.
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inv_std2 = (int16_t) WEBRTC_SPL_MUL_16_16_RSFT(tmp16, tmp16, 2);
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// TODO(bjornv): Investigate if changing to
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// |inv_std2| = (int16_t) WEBRTC_SPL_MUL_16_16_RSFT(|inv_std|, |inv_std|, 6);
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// gives better accuracy.
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tmp16 = WEBRTC_SPL_LSHIFT_W16(in_sample, 3); // Q7
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tmp16 = tmp16 - mean; // Q7 - Q7 = Q7
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tmp16 = (input << 3); // Q4 -> Q7
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tmp16 = tmp16 - mean; // Q7 - Q7 = Q7
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// To be used later, when updating noise/speech model
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// delta = (x-m)/std^2, in Q11
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*delta = (WebRtc_Word16)WEBRTC_SPL_MUL_16_16_RSFT(tmpDiv2, tmp16, 10); //(Q14*Q7)>>10 = Q11
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// To be used later, when updating noise/speech model.
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// |delta| = (x - m) / s^2, in Q11.
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// Q-domain: (Q14 * Q7) >> 10 = Q11.
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*delta = (int16_t) WEBRTC_SPL_MUL_16_16_RSFT(inv_std2, tmp16, 10);
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// Calculate tmp32=(x-m)^2/(2*std^2), in Q10
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tmp32 = (WebRtc_Word32)WEBRTC_SPL_MUL_16_16_RSFT(*delta, tmp16, 9); // One shift for /2
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// Calculate the exponent |tmp32| = (x - m)^2 / (2 * s^2), in Q10. Replacing
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// division by two with one shift.
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// Q-domain: (Q11 * Q7) >> 8 = Q10.
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tmp32 = WEBRTC_SPL_MUL_16_16_RSFT(*delta, tmp16, 9);
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// Calculate expVal ~= exp(-(x-m)^2/(2*std^2)) ~= exp2(-log2(exp(1))*tmp32)
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if (tmp32 < kCompVar)
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{
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// Calculate tmp16 = log2(exp(1))*tmp32 , in Q10
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tmp16 = (WebRtc_Word16)WEBRTC_SPL_MUL_16_16_RSFT((WebRtc_Word16)tmp32,
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kLog10Const, 12);
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tmp16 = -tmp16;
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tmp16_2 = (WebRtc_Word16)(0x0400 | (tmp16 & 0x03FF));
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tmp16_1 = (WebRtc_Word16)(tmp16 ^ 0xFFFF);
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tmp16 = (WebRtc_Word16)WEBRTC_SPL_RSHIFT_W16(tmp16_1, 10);
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tmp16 += 1;
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// Calculate expVal=log2(-tmp32), in Q10
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expVal = (WebRtc_Word16)WEBRTC_SPL_RSHIFT_W32((WebRtc_Word32)tmp16_2, tmp16);
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// If the exponent is small enough to give a non-zero probability we calculate
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// |exp_value| ~= exp(-(x - m)^2 / (2 * s^2))
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// ~= exp2(-log2(exp(1)) * |tmp32|).
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if (tmp32 < kCompVar) {
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// Calculate |tmp16| = log2(exp(1)) * |tmp32|, in Q10.
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// Q-domain: (Q12 * Q10) >> 12 = Q10.
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tmp16 = (int16_t) WEBRTC_SPL_MUL_16_16_RSFT(kLog2Exp, (int16_t) tmp32, 12);
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tmp16 = -tmp16;
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exp_value = (0x0400 | (tmp16 & 0x03FF));
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tmp16 ^= 0xFFFF;
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tmp16 >>= 10;
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tmp16 += 1;
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// Get |exp_value| = exp(-|tmp32|) in Q10.
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exp_value >>= tmp16;
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}
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} else
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{
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expVal = 0;
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}
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// Calculate y32=(1/std)*exp(-(x-m)^2/(2*std^2)), in Q20
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y32 = WEBRTC_SPL_MUL_16_16(tmpDiv, expVal); // Q10 * Q10 = Q20
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return y32; // Q20
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// Calculate and return (1 / s) * exp(-(x - m)^2 / (2 * s^2)), in Q20.
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// Q-domain: Q10 * Q10 = Q20.
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return WEBRTC_SPL_MUL_16_16(inv_std, exp_value);
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}
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@ -8,40 +8,32 @@
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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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// Gaussian probability calculations internally used in vad_core.c.
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/*
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* This header file includes the description of the internal VAD call
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* WebRtcVad_GaussianProbability.
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*/
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#ifndef WEBRTC_VAD_GMM_H_
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#define WEBRTC_VAD_GMM_H_
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#ifndef WEBRTC_COMMON_AUDIO_VAD_VAD_GMM_H_
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#define WEBRTC_COMMON_AUDIO_VAD_VAD_GMM_H_
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#include "typedefs.h"
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/****************************************************************************
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* WebRtcVad_GaussianProbability(...)
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*
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* This function calculates the probability for the value 'in_sample', given that in_sample
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* comes from a normal distribution with mean 'mean' and standard deviation 'std'.
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*
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* Input:
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* - in_sample : Input sample in Q4
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* - mean : mean value in the statistical model, Q7
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* - std : standard deviation, Q7
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*
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* Output:
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*
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* - delta : Value used when updating the model, Q11
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*
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* Return:
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* - out : out = 1/std * exp(-(x-m)^2/(2*std^2));
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* Probability for x.
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*
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*/
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WebRtc_Word32 WebRtcVad_GaussianProbability(WebRtc_Word16 in_sample,
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WebRtc_Word16 mean,
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WebRtc_Word16 std,
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WebRtc_Word16 *delta);
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// Calculates the probability for |input|, given that |input| comes from a
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// normal distribution with mean and standard deviation (|mean|, |std|).
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//
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// Inputs:
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// - input : input sample in Q4.
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// - mean : mean input in the statistical model, Q7.
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// - std : standard deviation, Q7.
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//
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// Output:
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//
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// - delta : input used when updating the model, Q11.
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// |delta| = (|input| - |mean|) / |std|^2.
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//
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// Return:
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// (probability for |input|) =
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// 1 / |std| * exp(-(|input| - |mean|)^2 / (2 * |std|^2));
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int32_t WebRtcVad_GaussianProbability(int16_t input,
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int16_t mean,
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int16_t std,
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int16_t* delta);
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#endif // WEBRTC_VAD_GMM_H_
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#endif // WEBRTC_COMMON_AUDIO_VAD_VAD_GMM_H_
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@ -8,13 +8,20 @@
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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 <stddef.h> // size_t
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#include <stddef.h> // size_t
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#include <stdlib.h>
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#include "gtest/gtest.h"
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#include "typedefs.h"
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#include "webrtc_vad.h"
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#ifdef __cplusplus
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extern "C"
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{
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#include "vad_gmm.h"
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}
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#endif
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namespace webrtc {
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namespace {
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const int16_t kModes[] = { 0, 1, 2, 3 };
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@ -152,6 +159,29 @@ TEST_F(VadTest, ApiTest) {
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EXPECT_EQ(0, WebRtcVad_Free(handle));
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}
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TEST_F(VadTest, GMMTests) {
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int16_t delta = 0;
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// Input value at mean.
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EXPECT_EQ(1048576, WebRtcVad_GaussianProbability(0, 0, 128, &delta));
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EXPECT_EQ(0, delta);
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EXPECT_EQ(1048576, WebRtcVad_GaussianProbability(16, 128, 128, &delta));
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EXPECT_EQ(0, delta);
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EXPECT_EQ(1048576, WebRtcVad_GaussianProbability(-16, -128, 128, &delta));
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EXPECT_EQ(0, delta);
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// Largest possible input to give non-zero probability.
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EXPECT_EQ(1024, WebRtcVad_GaussianProbability(59, 0, 128, &delta));
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EXPECT_EQ(7552, delta);
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EXPECT_EQ(1024, WebRtcVad_GaussianProbability(75, 128, 128, &delta));
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EXPECT_EQ(7552, delta);
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EXPECT_EQ(1024, WebRtcVad_GaussianProbability(-75, -128, 128, &delta));
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EXPECT_EQ(-7552, delta);
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// Too large input, should give zero probability.
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EXPECT_EQ(0, WebRtcVad_GaussianProbability(105, 0, 128, &delta));
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EXPECT_EQ(13440, delta);
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}
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// TODO(bjornv): Add a process test, run on file.
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} // namespace
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