使用python对音频信号进行降噪

源码链接：https://download.csdn.net/download/bibinGee/12327301

此算法基于（但不是完全重现）Audacity概述的一种降噪效果的算法（链接到C ++代码）
该算法需要两个输入：

包含音频片段原型噪声的噪声音频片段
包含要删除的信号和噪声的信号音频片段

算法步骤

在噪声音频片段上计算FFT
统计信息是通过噪声的FFT计算得出的（频率）
基于噪声的统计信息（和算法的期望灵敏度）计算阈值
通过信号计算FFT
通过将信号FFT与阈值进行比较来确定掩码
使用滤镜在频率和时间上对蒙版进行平滑处理
掩码被叠加到信号的FFT中，并被反转

wav_loc = "assets/audio/fish.wav"
rate, data = wavfile.read(wav_loc)
data = data / 32768

def fftnoise(f):f = np.array(f, dtype="complex")Np = (len(f) - 1) // 2phases = np.random.rand(Np) * 2 * np.piphases = np.cos(phases) + 1j * np.sin(phases)f[1 : Np + 1] *= phasesf[-1 : -1 - Np : -1] = np.conj(f[1 : Np + 1])return np.fft.ifft(f).realdef band_limited_noise(min_freq, max_freq, samples=1024, samplerate=1):freqs = np.abs(np.fft.fftfreq(samples, 1 / samplerate))f = np.zeros(samples)f[np.logical_and(freqs >= min_freq, freqs <= max_freq)] = 1

IPython.display.Audio(data=data, rate=rate)

fig, ax = plt.subplots(figsize=(20,4))
ax.plot(data)

加入噪声

noise_len = 2 # seconds
noise = band_limited_noise(min_freq=4000, max_freq = 12000, samples=len(data), samplerate=rate)*10
noise_clip = noise[:rate*noise_len]
audio_clip_band_limited = data+noise
fig, ax = plt.subplots(figsize=(20,4))
ax.plot(audio_clip_band_limited)
IPython.display.Audio(data=audio_clip_band_limited, rate=rate)

加入噪声后的波形：

去噪

import time
from datetime import timedelta as tddef _stft(y, n_fft, hop_length, win_length):return librosa.stft(y=y, n_fft=n_fft, hop_length=hop_length, win_length=win_length)def _istft(y, hop_length, win_length):return librosa.istft(y, hop_length, win_length)def _amp_to_db(x):return librosa.core.amplitude_to_db(x, ref=1.0, amin=1e-20, top_db=80.0)def _db_to_amp(x,):return librosa.core.db_to_amplitude(x, ref=1.0)def plot_spectrogram(signal, title):fig, ax = plt.subplots(figsize=(20, 4))cax = ax.matshow(signal,origin="lower",aspect="auto",cmap=plt.cm.seismic,vmin=-1 * np.max(np.abs(signal)),vmax=np.max(np.abs(signal)),)fig.colorbar(cax)ax.set_title(title)plt.tight_layout()plt.show()def plot_statistics_and_filter(mean_freq_noise, std_freq_noise, noise_thresh, smoothing_filter
):fig, ax = plt.subplots(ncols=2, figsize=(20, 4))plt_mean, = ax[0].plot(mean_freq_noise, label="Mean power of noise")plt_std, = ax[0].plot(std_freq_noise, label="Std. power of noise")plt_std, = ax[0].plot(noise_thresh, label="Noise threshold (by frequency)")ax[0].set_title("Threshold for mask")ax[0].legend()cax = ax[1].matshow(smoothing_filter, origin="lower")fig.colorbar(cax)ax[1].set_title("Filter for smoothing Mask")plt.show()def removeNoise(audio_clip,noise_clip,n_grad_freq=2,n_grad_time=4,n_fft=2048,win_length=2048,hop_length=512,n_std_thresh=1.5,prop_decrease=1.0,verbose=False,visual=False,
):"""Remove noise from audio based upon a clip containing only noiseArgs:audio_clip (array): The first parameter.noise_clip (array): The second parameter.n_grad_freq (int): how many frequency channels to smooth over with the mask.n_grad_time (int): how many time channels to smooth over with the mask.n_fft (int): number audio of frames between STFT columns.win_length (int): Each frame of audio is windowed by `window()`. The window will be of length `win_length` and then padded with zeros to match `n_fft`..hop_length (int):number audio of frames between STFT columns.n_std_thresh (int): how many standard deviations louder than the mean dB of the noise (at each frequency level) to be considered signalprop_decrease (float): To what extent should you decrease noise (1 = all, 0 = none)visual (bool): Whether to plot the steps of the algorithmReturns:array: The recovered signal with noise subtracted"""if verbose:start = time.time()# STFT over noisenoise_stft = _stft(noise_clip, n_fft, hop_length, win_length)noise_stft_db = _amp_to_db(np.abs(noise_stft))  # convert to dB# Calculate statistics over noisemean_freq_noise = np.mean(noise_stft_db, axis=1)std_freq_noise = np.std(noise_stft_db, axis=1)noise_thresh = mean_freq_noise + std_freq_noise * n_std_threshif verbose:print("STFT on noise:", td(seconds=time.time() - start))start = time.time()# STFT over signalif verbose:start = time.time()sig_stft = _stft(audio_clip, n_fft, hop_length, win_length)sig_stft_db = _amp_to_db(np.abs(sig_stft))if verbose:print("STFT on signal:", td(seconds=time.time() - start))start = time.time()# Calculate value to mask dB tomask_gain_dB = np.min(_amp_to_db(np.abs(sig_stft)))print(noise_thresh, mask_gain_dB)# Create a smoothing filter for the mask in time and frequencysmoothing_filter = np.outer(np.concatenate([np.linspace(0, 1, n_grad_freq + 1, endpoint=False),np.linspace(1, 0, n_grad_freq + 2),])[1:-1],np.concatenate([np.linspace(0, 1, n_grad_time + 1, endpoint=False),np.linspace(1, 0, n_grad_time + 2),])[1:-1],)smoothing_filter = smoothing_filter / np.sum(smoothing_filter)# calculate the threshold for each frequency/time bindb_thresh = np.repeat(np.reshape(noise_thresh, [1, len(mean_freq_noise)]),np.shape(sig_stft_db)[1],axis=0,).T# mask if the signal is above the thresholdsig_mask = sig_stft_db < db_threshif verbose:print("Masking:", td(seconds=time.time() - start))start = time.time()# convolve the mask with a smoothing filtersig_mask = scipy.signal.fftconvolve(sig_mask, smoothing_filter, mode="same")sig_mask = sig_mask * prop_decreaseif verbose:print("Mask convolution:", td(seconds=time.time() - start))start = time.time()# mask the signalsig_stft_db_masked = (sig_stft_db * (1 - sig_mask)+ np.ones(np.shape(mask_gain_dB)) * mask_gain_dB * sig_mask)  # mask realsig_imag_masked = np.imag(sig_stft) * (1 - sig_mask)sig_stft_amp = (_db_to_amp(sig_stft_db_masked) * np.sign(sig_stft)) + (1j * sig_imag_masked)if verbose:print("Mask application:", td(seconds=time.time() - start))start = time.time()# recover the signalrecovered_signal = _istft(sig_stft_amp, hop_length, win_length)recovered_spec = _amp_to_db(np.abs(_stft(recovered_signal, n_fft, hop_length, win_length)))if verbose:print("Signal recovery:", td(seconds=time.time() - start))if visual:plot_spectrogram(noise_stft_db, title="Noise")if visual:plot_statistics_and_filter(mean_freq_noise, std_freq_noise, noise_thresh, smoothing_filter)if visual:plot_spectrogram(sig_stft_db, title="Signal")if visual:plot_spectrogram(sig_mask, title="Mask applied")if visual:plot_spectrogram(sig_stft_db_masked, title="Masked signal")if visual:plot_spectrogram(recovered_spec, title="Recovered spectrogram")return recovered_signal

output = removeNoise(audio_clip=audio_clip_band_limited,noise_clip=noise_clip,verbose=True,visual=True)

fig, ax = plt.subplots(nrows=1,ncols=1, figsize=(20,4))
plt.plot(output, color='black')
ax.set_xlim((0, len(output)))
plt.show()
# play back a sample of the song
IPython.display.Audio(data=output, rate=44100)

去噪后的波形：

让我们尝试一个更具挑战性的示例

wav_loc = "assets/audio/fish.wav"
src_rate, src_data = wavfile.read(wav_loc)
# src_data = np.concatenate((src_data, np.zeros(src_rate*3)))
src_data = src_data / 32768
wav_loc = "assets/audio/cafe.wav"
noise_rate, noise_data = wavfile.read(wav_loc)
# get some noise to add to the signal
noise_to_add = noise_data[len(src_data) : len(src_data) * 2]
# get a different part of the noise clip for calculating statistics
noise_clip = noise_data[: len(src_data)]
noise_clip = noise_clip / max(noise_to_add)
noise_to_add = noise_to_add / max(noise_to_add)
# apply noise
snr = 1  # signal to noise ratio
audio_clip_cafe = src_data + noise_to_add / snr
noise_clip = noise_clip / snr
fig, ax = plt.subplots(figsize=(20, 4))
ax.plot(noise_clip)
IPython.display.Audio(data=noise_clip, rate=src_rate)

强干扰噪声波形


fig, ax = plt.subplots(figsize=(20,4))
ax.plot(audio_clip_cafe)
IPython.display.Audio(data=audio_clip_cafe, rate=src_rate)

信号加上强干扰噪声后

执行去噪

output = removeNoise(audio_clip=audio_clip_cafe,noise_clip=noise_clip,n_std_thresh=2,prop_decrease=0.95,visual=True,
)fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(20, 4))
plt.plot(output, color="black")
ax.set_xlim((0, len(output)))
plt.show()
# play back a sample of the song
IPython.display.Audio(data=output, rate=44100)

去噪后的信号

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使用python对音频信号进行降噪

加入噪声

去噪

让我们尝试一个更具挑战性的示例

强干扰噪声波形

信号加上强干扰噪声后

去噪后的信号

使用python对音频信号进行降噪相关推荐

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