Question

我试图了解一个SpeechPy-Python库，用于从音频文件中提取功能。文档中有一个测试代码：

    import scipy.io.wavfile as wav
    import numpy as np
    import speechpy
    import os

    file_name = 
    os.path.join(os.path.dirname(os.path.abspath(__file__)),'Alesis- 
    Sanctuary-QCard-AcoustcBas-C2.wav')
    fs, signal = wav.read(file_name)
    signal = signal[:,0]

    # Example of pre-emphasizing.
    signal_preemphasized = speechpy.processing.preemphasis(signal, cof=0.98)

    # Example of staching frames
    frames = speechpy.processing.stack_frames(signal, sampling_frequency=fs, 
    frame_length=0.020, frame_stride=0.01, filter=lambda x: np.ones((x,)),
    zero_padding=True)

    # Example of extracting power spectrum
    power_spectrum = speechpy.processing.power_spectrum(frames, 
    fft_points=512)
    print('power spectrum shape=', power_spectrum.shape)

    ############# Extract MFCC features #############
    mfcc = speechpy.feature.mfcc(signal, sampling_frequency=fs, 
    frame_length=0.020, frame_stride=0.01,
     num_filters=40, fft_length=512, low_frequency=0, high_frequency=None)
    mfcc_cmvn = 
    speechpy.processing.cmvnw(mfcc,win_size=301,variance_normalization=True)
    print('mfcc(mean + variance normalized) feature shape=', 
    mfcc_cmvn.shape)

    mfcc_feature_cube = speechpy.feature.extract_derivative_feature(mfcc)
    print('mfcc feature cube shape=', mfcc_feature_cube.shape)

    ############# Extract logenergy features #############
    logenergy = speechpy.feature.lmfe(signal, sampling_frequency=fs, 
    frame_length=0.020, frame_stride=0.01,
     num_filters=40, fft_length=512, low_frequency=0, high_frequency=None)
    logenergy_feature_cube = 
    speechpy.feature.extract_derivative_feature(logenergy)
    print('logenergy features=', logenergy.shape)

所以我的主要问题是：当它最终提取特征时，我如何知道从音频文件中提取出的确切特征是什么？当我将音频文件放入此代码时，它将返回一个矩阵。但是，不是一个文件的所有功能都应该作为矢量返回吗？

如果有人帮助我理解这一点，将非常感谢。

SpeechPy准确提取哪些功能？

0 个答案: