音乐指纹识别（四）：提取特征进行识别

理论的基础有了接下来就是应用到具体的情况中，现在先通过对wav音频进行短时傅里叶变换，看看能得到什么样的图形。

#!/usr/bin/python
#coding=utf-8

import numpy as np

def calc_stft(signal, sample_rate=16000, frame_size=0.025, frame_stride=0.01, winfunc=np.hamming, NFFT=512):

    # Calculate the number of frames from the signal
    frame_length = frame_size * sample_rate
    frame_step = frame_stride * sample_rate
    signal_length = len(signal)
    frame_length = int(round(frame_length))
    frame_step = int(round(frame_step))
    num_frames = 1 + int(np.ceil(float(np.abs(signal_length - frame_length)) / frame_step))
    # zero padding
    pad_signal_length = num_frames * frame_step + frame_length
    z = np.zeros((pad_signal_length - signal_length))
    # Pad signal to make sure that all frames have equal number of samples
    # without truncating any samples from the original signal
    pad_signal = np.append(signal, z)

    # Slice the signal into frames from indices
    indices = np.tile(np.arange(0, frame_length), (num_frames, 1)) + \
            np.tile(np.arange(0, num_frames * frame_step, frame_step), (frame_length, 1)).T
    frames = pad_signal[indices.astype(np.int32, copy=False)]
    # Get windowed frames
    frames *= winfunc(frame_length)
    # Compute the one-dimensional n-point discrete Fourier Transform(DFT) of
    # a real-valued array by means of an efficient algorithm called Fast Fourier Transform (FFT)
    mag_frames = np.absolute(np.fft.rfft(frames, NFFT))
    # Compute power spectrum
    pow_frames = (1.0 / NFFT) * ((mag_frames) ** 2)

    return pow_frames

if __name__ == '__main__':
    import scipy.io.wavfile
    import matplotlib.pyplot as plt

    # Read wav file
    # "OSR_us_000_0010_8k.wav" is downloaded from http://www.voiptroubleshooter.com/open_speech/american.html
    sample_rate, signal = scipy.io.wavfile.read("OSR_us_000_0010_8k.wav")
    # Get speech data in the first 2 seconds
    signal = signal[0:int(2. * sample_rate)]

    # Calculate the short time fourier transform
    pow_spec = calc_stft(signal, sample_rate)

    plt.imshow(pow_spec)
    plt.tight_layout()
    plt.show()

图形如下：

横坐标表示时间，纵坐标表示频率，每个时间点对应的是一个频率范围，在实际的时候可以这样来做，一个时间点取一个局部频率的最大值，代表这个时间点的特征，对时间点和时间点频率的结合做哈希值，来代表一个特征。

h = hashlib.sha1("%s|%s|%s" % (str(freq1), str(freq2), str(t_delta)))

把每个特征点存放起来，供以后的查询。

如图更能说明特征点的取法：

这样在这个过程中，特征点的选取已经完成，后续只要对查找的音频用同样的方法生成特征值，在进行特征值比对，就能完成查询的功能。