从soundfile获取帧

Question

有谁知道如何将声音文件拆分成帧，然后可以将这些帧转换为numpy数组，详细说明帧中的特定声音频率？

例如，使用cv2，我可以将影片剪辑拆分为多个帧，然后将这些帧存储为图像库。这段代码做得很好，在某种程度上，我可以很容易地获得每个图像的颜色直方图。

filepath1 = input('Please enter the filepath for where the frames should be saved: ') 

name = input('Please enter the name of the clip: ') 

ret, frame = clip.read()
count = 0
ret == True
while ret:
    ret, frame = clip.read()
    cv2.imwrite(os.path.join(filepath1,name+'%d.png'%count), frame)
    count += 1

但我似乎找不到任何同样简单的声音文件；有谁有任何建议，如何(或是否)可以做到？

Answer 1

严格地说，相当于影片帧的声音文件是音频样本。这只是每个通道的一个值，所以我不确定这是否是您真正想要的。我最好的猜测是分析文件的频率内容是如何随着时间的推移而变化的。

也许你想看看spectrogram？在这种情况下，取自www.frank-zalkow.de的以下脚本可能会执行您想要的操作，或者至少为您提供一些如何开始的思路。

​

#!/usr/bin/env python
#coding: utf-8
""" This work is licensed under a Creative Commons Attribution 3.0 Unported License.
    Frank Zalkow, 2012-2013 """

import numpy as np
from matplotlib import pyplot as plt
import scipy.io.wavfile as wav
from numpy.lib import stride_tricks

""" short time fourier transform of audio signal """
def stft(sig, frameSize, overlapFac=0.5, window=np.hanning):
    win = window(frameSize)
    hopSize = int(frameSize - np.floor(overlapFac * frameSize))

    # zeros at beginning (thus center of 1st window should be for sample nr. 0)
    samples = np.append(np.zeros(np.floor(frameSize/2.0)), sig)    
    # cols for windowing
    cols = np.ceil( (len(samples) - frameSize) / float(hopSize)) + 1
    # zeros at end (thus samples can be fully covered by frames)
    samples = np.append(samples, np.zeros(frameSize))

    frames = stride_tricks.as_strided(samples, shape=(cols, frameSize), strides=(samples.strides[0]*hopSize, samples.strides[0])).copy()
    frames *= win

    return np.fft.rfft(frames)    

""" scale frequency axis logarithmically """    
def logscale_spec(spec, sr=44100, factor=20.):
    timebins, freqbins = np.shape(spec)

    scale = np.linspace(0, 1, freqbins) ** factor
    scale *= (freqbins-1)/max(scale)
    scale = np.unique(np.round(scale))

    # create spectrogram with new freq bins
    newspec = np.complex128(np.zeros([timebins, len(scale)]))
    for i in range(0, len(scale)):
        if i == len(scale)-1:
            newspec[:,i] = np.sum(spec[:,scale[i]:], axis=1)
        else:        
            newspec[:,i] = np.sum(spec[:,scale[i]:scale[i+1]], axis=1)

    # list center freq of bins
    allfreqs = np.abs(np.fft.fftfreq(freqbins*2, 1./sr)[:freqbins+1])
    freqs = []
    for i in range(0, len(scale)):
        if i == len(scale)-1:
            freqs += [np.mean(allfreqs[scale[i]:])]
        else:
            freqs += [np.mean(allfreqs[scale[i]:scale[i+1]])]

    return newspec, freqs

""" plot spectrogram"""
def plotstft(audiopath, binsize=2**10, plotpath=None, colormap="jet"):
    samplerate, samples = wav.read(audiopath)
    s = stft(samples, binsize)

    sshow, freq = logscale_spec(s, factor=1.0, sr=samplerate)
    ims = 20.*np.log10(np.abs(sshow)/10e-6) # amplitude to decibel

    timebins, freqbins = np.shape(ims)

    plt.figure(figsize=(15, 7.5))
    plt.imshow(np.transpose(ims), origin="lower", aspect="auto", cmap=colormap, interpolation="none")
    plt.colorbar()

    plt.xlabel("time (s)")
    plt.ylabel("frequency (hz)")
    plt.xlim([0, timebins-1])
    plt.ylim([0, freqbins])

    xlocs = np.float32(np.linspace(0, timebins-1, 5))
    plt.xticks(xlocs, ["%.02f" % l for l in ((xlocs*len(samples)/timebins)+(0.5*binsize))/samplerate])
    ylocs = np.int16(np.round(np.linspace(0, freqbins-1, 10)))
    plt.yticks(ylocs, ["%.02f" % freq[i] for i in ylocs])

    if plotpath:
        plt.savefig(plotpath, bbox_inches="tight")
    else:
        plt.show()

    plt.clf()

plotstft("my_audio_file.wav")