利用FFT对wav格式的文件进行解析

1.读取wav文件

# -*- coding: utf-8 -*-

import wave

import pylab as pl

import numpy as np

# 打开WAV文档

f = wave.open(r"c:\WINDOWS\Media\ding.wav", "rb")

# 读取格式信息

# (nchannels, sampwidth, framerate, nframes, comptype, compname)

params = f.getparams()

nchannels, sampwidth, framerate, nframes = params[:4]

# 读取波形数据

str_data = f.readframes(nframes)

f.close()

#将波形数据转换为数组

wave_data = np.fromstring(str_data, dtype=np.short)

wave_data.shape = -1, 2

wave_data = wave_data.T

time = np.arange(0, nframes) * (1.0 / framerate)

# 绘制键闹悉波形

pl.subplot(211)

pl.plot(time, wave_data[0])

pl.subplot(212)

pl.plot(time, wave_data[1], c="g")

pl.xlabel("time (seconds)")

pl.show()

2.观察信号频谱

# -*- coding: utf-8 -*-

import numpy as np

import pylab as pl

sampling_rate = 8000

fft_size = 512

t = np.arange(0, 1.0, 1.0/sampling_rate)

x = np.sin(2*np.pi*156.25*t) + 2*np.sin(2*np.pi*234.375*t)

xs = x[:fft_size]

xf = np.fft.rfft(xs)/fft_size

freqs = np.linspace(0, sampling_rate/2, fft_size/2+1)

xfp = 20*np.log10(np.clip(np.abs(xf), 1e-20, 1e100))

pl.figure(figsize=(8,4))

pl.subplot(211)

pl.plot(t[:fft_size], xs)

pl.xlabel(u"时弯脊间(秒)")

pl.title(u"156.25Hz和234.375Hz的波形和频谱")

pl.subplot(212)

pl.plot(freqs, xfp)

pl.xlabel(u"频率(Hz)")

pl.subplots_adjust(hspace=0.4)

pl.show()

X=fft(x)X1=fft(x1)% 求信号的频谱

X=X(1:length(X)/2)X1=X1(1:length(X1)/2) % 截取前半部分

deltaf=fs/2/length(X)% 计算频谱的谱线间隔

f=0:deltaf:fs/2-deltaf % 计算频谱信悄猛频率范围

subplot(222)plot(f,abs(X)) % 绘制频谱图

title('语音信号幅度谱图')xlabel('频率（单位：Hz）')ylabel('幅度谱') axis tight

subplot(224)plot(f,abs(X1)) % 绘制运芦频谱图

title('加入干扰后的语音信号幅度谱图')xlabel('频率（单位：Hz）')ylabel('幅度谱') axis tight

PS:这本书还没有出版滑桥，我只能给你透露这么多了。