Python谱减法语音降噪实例

Python  /  管理员 发布于 7年前   222

代码中用到了nextpow2，其中n = nextpow2(x) 表示最接近x的2的n次幂。

#!/usr/bin/env pythonimport numpy as npimport waveimport nextpow2import math  # 打开WAV文档f = wave.open("filename.wav")# 读取格式信息# (nchannels, sampwidth, framerate, nframes, comptype, compname)params = f.getparams()nchannels, sampwidth, framerate, nframes = params[:4]fs = framerate# 读取波形数据str_data = f.readframes(nframes)f.close()# 将波形数据转换为数组x = np.fromstring(str_data, dtype=np.short)# 计算参数len_ = 20 * fs // 1000PERC = 50len1 = len_ * PERC // 100len2 = len_ - len1# 设置默认参数Thres = 3Expnt = 2.0beta = 0.002G = 0.9# 初始化汉明窗win = np.hamming(len_)# normalization gain for overlap+add with 50% overlapwinGain = len2 / sum(win)  # Noise magnitude calculations - assuming that the first 5 frames is noise/silencenFFT = 2 * 2 ** (nextpow2.nextpow2(len_))noise_mean = np.zeros(nFFT)  j = 0for k in range(1, 6):  noise_mean = noise_mean + abs(np.fft.fft(win * x[j:j + len_], nFFT))  j = j + len_noise_mu = noise_mean / 5  # --- allocate memory and initialize various variablesk = 1img = 1jx_old = np.zeros(len1)Nframes = len(x) // len2 - 1xfinal = np.zeros(Nframes * len2)  # =========================  Start Processing  ===============================for n in range(0, Nframes):  # Windowing  insign = win * x[k-1:k + len_ - 1]  # compute fourier transform of a frame  spec = np.fft.fft(insign, nFFT)  # compute the magnitude  sig = abs(spec)    # save the noisy phase information  theta = np.angle(spec)  SNRseg = 10 * np.log10(np.linalg.norm(sig, 2) ** 2 / np.linalg.norm(noise_mu, 2) ** 2)      def berouti(SNR):    if -5.0 <= SNR <= 20.0:      a = 4 - SNR * 3 / 20    else:      if SNR < -5.0:        a = 5      if SNR > 20:        a = 1    return a      def berouti1(SNR):    if -5.0 <= SNR <= 20.0:      a = 3 - SNR * 2 / 20    else:      if SNR < -5.0:        a = 4      if SNR > 20:        a = 1    return a    if Expnt == 1.0: # 幅度谱    alpha = berouti1(SNRseg)  else: # 功率谱    alpha = berouti(SNRseg)  #############  sub_speech = sig ** Expnt - alpha * noise_mu ** Expnt;  # 当纯净信号小于噪声信号的功率时  diffw = sub_speech - beta * noise_mu ** Expnt  # beta negative components    def find_index(x_list):    index_list = []    for i in range(len(x_list)):      if x_list[i] < 0:        index_list.append(i)    return index_list    z = find_index(diffw)  if len(z) > 0:    # 用估计出来的噪声信号表示下限值    sub_speech[z] = beta * noise_mu[z] ** Expnt    # --- implement a simple VAD detector --------------    if SNRseg < Thres: # Update noise spectrum      noise_temp = G * noise_mu ** Expnt + (1 - G) * sig ** Expnt # 平滑处理噪声功率谱      noise_mu = noise_temp ** (1 / Expnt) # 新的噪声幅度谱    # flipud函数实现矩阵的上下翻转，是以矩阵的“水平中线”为对称轴    # 交换上下对称元素    sub_speech[nFFT // 2 + 1:nFFT] = np.flipud(sub_speech[1:nFFT // 2])    x_phase = (sub_speech ** (1 / Expnt)) * (np.array([math.cos(x) for x in theta]) + img * (np.array([math.sin(x) for x in theta])))    # take the IFFT      xi = np.fft.ifft(x_phase).real    # --- Overlap and add ---------------    xfinal[k-1:k + len2 - 1] = x_old + xi[0:len1]    x_old = xi[0 + len1:len_]    k = k + len2# 保存文件wf = wave.open('outfile.wav', 'wb')# 设置参数wf.setparams(params)# 设置波形文件 .tostring()将array转换为datawave_data = (winGain * xfinal).astype(np.short)wf.writeframes(wave_data.tostring())wf.close()

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