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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）。

我以前也从未使用过信号、FFT 或 WAV 格式，所以这对我来说是新的、未知的领域。我将通道检索为以 44100 Hz 采样的有符号 16 位整数列表

  1. 在磁盘 .wav 文件上
  2. 从我的笔记本电脑播放录制的音乐

然后我通过一个窗口（2 ^ k）进行每个窗口，并有一定的重叠。对于每个窗口，如下所示：

# 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（至少对我而言）发生在我的评论 [[[[ 这是我不确定的地方]]]] 的行中。

谁能指出我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？

4

1 回答 1

1

快速浏览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 Window (hamming, hann)

np.hamming(len(window_samples)) * window_samples

然后你可以计算rfft。

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)，这条线没有意义：

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

可以为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;
于 2013-08-31T21:55:47.217 回答