回声消除（AEC）原理、算法及实战——BNLMS（Block Normalized Least Mean Square）

 BNLMS结合了BLMS和NLMS各自的优点，具体算法如下

代码如下：

import numpy as np
import librosa
import soundfile as sf
import pyroomacoustics as pra

from scipy.linalg import hankel

def bnlms(x, d, N = 4, L=4, mu = 0.1):
  beta = 0.9
  nIters = min(len(x),len(d))//L
  u = np.zeros(L+N-1)
  h = np.zeros(N)
  e = np.zeros(nIters*L)
  norm = np.full(L,1e-3)
  for n in range(nIters):
    u[:-L] = u[L:]
    u[-L:] = x[n*L:(n+1)*L]
    d_n = d[n*L:(n+1)*L]
    A = hankel(u[:L],u[-N:])
    e_n = d_n - np.dot(A,h)
    norm = beta*norm + (1-beta)*(np.sum(A**2,axis=1))
    h = h + mu*np.dot(A.T/(norm+1e-3),e_n)/L
    e[n*L:(n+1)*L] = e_n
  return e



# x 原始参考信号
# v 理想mic信号 
# 生成模拟的mic信号和参考信号
def creat_sim_sound(x,v):
    rt60_tgt = 0.08
    room_dim = [2, 2, 2]

    e_absorption, max_order = pra.inverse_sabine(rt60_tgt, room_dim)
    room = pra.ShoeBox(room_dim, fs=sr, materials=pra.Material(e_absorption), max_order=max_order)
    room.add_source([1.5, 1.5, 1.5])
    room.add_microphone([0.1, 0.5, 0.1])
    room.compute_rir()
    rir = room.rir[0][0]
    rir = rir[np.argmax(rir):]
    # x 经过房间反射得到 y
    y = np.convolve(x,rir)
    scale = np.sqrt(np.mean(x**2)) /  np.sqrt(np.mean(y**2))
    # y 为经过反射后到达麦克风的声音
    y = y*scale

    L = max(len(y),len(v))
    y = np.pad(y,[0,L-len(y)])
    v = np.pad(v,[L-len(v),0])
    x = np.pad(x,[0,L-len(x)])
    d = v + y
    return x,d

if __name__ == "__main__":
    x_org, sr  = librosa.load('female.wav',sr=8000)
    v_org, sr  = librosa.load('male.wav',sr=8000)

    x,d = creat_sim_sound(x_org,v_org)

    e = bnlms(x, d, N=256, L=4, mu=0.1)
    e = np.clip(e,-1,1)
    sf.write('x.wav', x, sr, subtype='PCM_16')
    sf.write('d.wav', d, sr, subtype='PCM_16')
    sf.write('blms.wav', e, sr, subtype='PCM_16')

参考资料：

https://www.bilibili.com/video/BV1gt4y1E7yz/?spm_id_from=333.788&vd_source=77c874a500ef21df351103560dada737