WebRTC AEC快適ノイズアルゴリズムの追加

WebRTCの快適ノイズは、実際には、同じフレームのエコー出力NLPパラメータの重み付け処理によって発生したノイズである.32 K信号であれば、生成された高周波数帯域のランダムノイズに対して統一的に重み付けされ、重み付け値は同じフレームのエコーキャンセル出力NLPパラメータの平均値である.

static void ComfortNoise(AecCore* aec,
                         float efw[2][PART_LEN1],
                         complex_t* comfortNoiseHband,
                         const float* noisePow,
                         const float* lambda) {
  int i, num;
  float rand[PART_LEN];
  float noise, noiseAvg, tmp, tmpAvg;
  int16_t randW16[PART_LEN];
  complex_t u[PART_LEN1];

  const float pi2 = 6.28318530717959f;

  // Generate a uniform random array on [0 1]
  //   seed    part = 64        ，   randW16    ，         
  WebRtcSpl_RandUArray(randW16, PART_LEN, &aec->seed);
  //                   rand 
  for (i = 0; i < PART_LEN; i++) {
    rand[i] = ((float)randW16[i]) / 32768;
  }

  // Reject LF noise
  u[0][0] = 0;
  u[0][1] = 0;
  //                      
  for (i = 1; i < PART_LEN1; i++) {
	  // 2*π*f = w，           
    tmp = pi2 * rand[i - 1];

	//            ，         
    noise = sqrtf(noisePow[i]);
	//     ，       u 
    u[i][0] = noise * cosf(tmp);
    u[i][1] = -noise * sinf(tmp);
  }
  u[PART_LEN][1] = 0;

  for (i = 0; i < PART_LEN1; i++) {
    //       lambda         ，            u
    tmp = sqrtf(WEBRTC_SPL_MAX(1 - lambda[i] * lambda[i], 0));
    efw[0][i] += tmp * u[i][0];
    efw[1][i] += tmp * u[i][1];
  }

  //         
  // TODO: don't compute noise and "tmp" twice. Use the previous results.
  noiseAvg = 0.0;
  tmpAvg = 0.0;
  num = 0;
  if (aec->sampFreq == 32000 && flagHbandCn == 1) {

    // average noise scale
    // average over second half of freq spectrum (i.e., 4->8khz)
    // TODO: we shouldn't need num. We know how many elements we're summing.
	  //          noisePow          
    for (i = PART_LEN1 >> 1; i < PART_LEN1; i++) {
      num++;
      noiseAvg += sqrtf(noisePow[i]);
    }
    noiseAvg /= (float)num;

    // average nlp scale
    // average over second half of freq spectrum (i.e., 4->8khz)
    // TODO: we shouldn't need num. We know how many elements we're summing.
	 //         lambda         
    num = 0;
    for (i = PART_LEN1 >> 1; i < PART_LEN1; i++) {
      num++;
      tmpAvg += sqrtf(WEBRTC_SPL_MAX(1 - lambda[i] * lambda[i], 0));
    }
    tmpAvg /= (float)num;

    //                    
    // TODO: we should probably have a new random vector here.
    // Reject LF noise
    u[0][0] = 0;
    u[0][1] = 0;
    for (i = 1; i < PART_LEN1; i++) {
      tmp = pi2 * rand[i - 1];

      // Use average noise for H band
      u[i][0] = noiseAvg * (float)cos(tmp);
      u[i][1] = -noiseAvg * (float)sin(tmp);
    }
    u[PART_LEN][1] = 0;

    for (i = 0; i < PART_LEN1; i++) {
      //        lambda               
      comfortNoiseHband[i][0] = tmpAvg * u[i][0];
      comfortNoiseHband[i][1] = tmpAvg * u[i][1];
    }
  }
}