python之计算 wav 文件和录制声音的频谱图(音量归一化)_编程设计

我想以一致的方式比较录制的音频和从磁盘读取的音频，但我遇到了音量标准化的问题(否则频谱图的幅度不同)。

我也从未使用过信号、FFT 或 WAV 格式，所以这对我来说是全新的、未知的领域。我将 channel 检索为以 44100 Hz 采样的有符号 16 位整数列表

磁盘上的 .wav 文件
录制的音乐从我的笔记本电脑播放

然后我通过一个窗口 (2^k) 进行每个操作，并有一定的重叠量。对于这样的每个窗口:

# calculate window variables 
window_step_size = int(self.window_size * (1.0 - self.window_overlap_ratio)) + 1 
last_frame = nframes - window_step_size # nframes is total number of frames from audio source 
num_windows, i = 0, 0 # calculate number of windows 
while i <= last_frame:  
    num_windows += 1 
    i += window_step_size 
 
# allocate memory and initialize counter 
wi = 0 # index 
nfft = 2 ** self.nextpowof2(self.window_size) # size of FFT in 2^k 
fft2D = np.zeros((nfft/2 + 1, num_windows), dtype='c16') # 2d array for storing results 
 
# for each window 
count = 0 
times = np.zeros((1, num_windows)) # num_windows was calculated 
 
while wi <= last_frame: 
 
    # channel_samples is simply list of signed ints 
    window_samples = channel_samples[ wi : (wi + self.window_size)] 
    window_samples = np.hamming(len(window_samples)) * window_samples  
 
    # calculate and reformat [[[[ THIS IS WHERE I'M UNSURE ]]]] 
    fft = 2 * np.fft.rfft(window_samples, n=nfft) / nfft 
    fft[0] = 0 # apparently these are completely real and should not be used 
    fft[nfft/2] = 0  
    fft = np.sqrt(np.square(fft) / np.mean(fft)) # use RMS of data 
    fft2D[:, count] = 10 * np.log10(np.absolute(fft)) 
 
    # sec / frame * frames = secs 
    # get midpt 
    times[0, count] = self.dt * wi 
 
    wi += window_step_size 
    count += 1 
 
# remove NaNs, infs 
whereAreNaNs = np.isnan(fft2D); 
fft2D[whereAreNaNs] = 0; 
whereAreInfs = np.isinf(fft2D); 
fft2D[whereAreInfs] = 0; 
 
# find the spectorgram peaks 
fft2D = fft2D.astype(np.float32) 
 
# the get_2D_peaks() method discretizes the fft2D periodogram array and then 
# finds peaks and filters out those peaks below the threshold supplied 
#  
# the `amp_xxxx` variables are used for discretizing amplitude and the  
# times array above is used to discretize the time into buckets 
local_maxima = self.get_2D_peaks(fft2D, self.amp_threshold, self.amp_max, self.amp_min, self.amp_step_size, times, self.dt)

特别是，疯狂的事情(至少对我来说)发生在我的评论[[[[这是我不确定的地方]]]]。

任何人都可以指出正确的方向或帮助我在正确标准化音量的同时生成此音频频谱图吗？

请您参考如下方法:

快速查看告诉我您忘记使用窗口，有必要计算您的 Spectrogram 。

您需要在“window_samples”中使用一个窗口(hamming、hann)

np.hamming(len(window_samples)) * window_samples

然后就可以计算rfft了。

编辑:

#calc magnetitude from FFT 
fftData=fft(windowed); 
#Get Magnitude (linear scale) of first half values 
Mag=abs(fftData(1:Chunk/2)) 
#if you want log scale R=20 * np.log10(Mag) 
plot(Mag)

#calc RMS from FFT
RMS = np.sqrt( (np.sum(np.abs(np.fft(data)**2) / len(data))) / (len(data) / 2) )

RMStoDb = 20 * log10(RMS)

PS:如果你想从 FFT 计算 RMS，你不能使用 Window(Hann, Hamming)，这行没有意义:

fft = np.sqrt(np.square(fft) / np.mean(fft)) # use RMS of data

可以对每个窗口做一个简单的归一化数据:

window_samples = channel_samples[ wi : (wi + self.window_size)] 
 
#framMax=np.max(window_samples); 
framMean=np.mean(window_samples); 
 
Normalized=window_samples/framMean;