Question

我想以一致的方式将录制的音频与从磁盘读取的音频进行比较，但我遇到了音量标准化的问题（否则频谱图的幅度不同）。

我以前从未使用过信号，FFT或WAV格式，所以这对我来说是一个新的，未知的领域。我检索通道作为带符号16位整数的列表从44100赫兹采样

在磁盘上.wav文件
从我的笔记本电脑播放录制的音乐

然后我用一个窗口（2 ^ k）进行每次重叠。对于每个窗口，如：

# 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，疯狂的东西（至少对我而言）发生在我的评论[[[[这是我不确定的地方]]]上。

任何人都能指出我正确的方向或帮我生成音频频谱图，同时正确调整音量吗？

Answer 1

快速查看告诉我您忘记使用窗口，有必要计算您的频谱图。

你需要在“window_samples”

中使用一个Window（汉明，汉）

np.hamming(len(window_samples)) * window_samples

然后你可以计算rfft。

修改

#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)


来自FFT的#calc RMS   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），这条线是没有意义的：

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

可以为每个窗口完成一个简单的规范化数据：

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

计算wav文件的频谱图＆amp;录制声音（音量标准化）

1 个答案: