Python譜減法語音降噪例項
阿新 • • 發佈:2020-01-09
程式碼中用到了nextpow2,其中n = nextpow2(x) 表示最接近x的2的n次冪。
#!/usr/bin/env python import numpy as np import wave import nextpow2 import math # 開啟WAV文件 f = wave.open("filename.wav") # 讀取格式資訊 # (nchannels,sampwidth,framerate,nframes,comptype,compname) params = f.getparams() nchannels,nframes = params[:4] fs = framerate # 讀取波形資料 str_data = f.readframes(nframes) f.close() # 將波形資料轉換為陣列 x = np.fromstring(str_data,dtype=np.short) # 計算引數 len_ = 20 * fs // 1000 PERC = 50 len1 = len_ * PERC // 100 len2 = len_ - len1 # 設定預設引數 Thres = 3 Expnt = 2.0 beta = 0.002 G = 0.9 # 初始化漢明窗 win = np.hamming(len_) # normalization gain for overlap+add with 50% overlap winGain = len2 / sum(win) # Noise magnitude calculations - assuming that the first 5 frames is noise/silence nFFT = 2 * 2 ** (nextpow2.nextpow2(len_)) noise_mean = np.zeros(nFFT) j = 0 for 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 variables k = 1 img = 1j x_old = np.zeros(len1) Nframes = len(x) // len2 - 1 xfinal = 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: # 幅度譜 alpha = berouti1(SNRseg) else: # 功率譜 alpha = berouti(SNRseg) ############# sub_speech = sig ** Expnt - alpha * noise_mu ** Expnt; # 當純淨訊號小於噪聲訊號的功率時 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: # 用估計出來的噪聲訊號表示下限值 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 # 平滑處理噪聲功率譜 noise_mu = noise_temp ** (1 / Expnt) # 新的噪聲幅度譜 # flipud函式實現矩陣的上下翻轉,是以矩陣的“水平中線”為對稱軸 # 交換上下對稱元素 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 # 儲存檔案 wf = wave.open('outfile.wav','wb') # 設定引數 wf.setparams(params) # 設定波形檔案 .tostring()將array轉換為data wave_data = (winGain * xfinal).astype(np.short) wf.writeframes(wave_data.tostring()) wf.close()
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