Python audio analysis (1) Draw the time domain frequency domain diagram of wav

Python audio analysis (1) Draw the time domain frequency domain diagram of wav

1. Introduction

Time domain graph: x axis is time, y axis is amplitude
Frequency domain graph: x axis is frequency, y axis is amplitude
(the above frequency domain graph is linear amplitude spectrum, there are also log amplitude spectrum, self power spectrum, y axis Not simply amplitude)

The intuitive relationship between time domain and frequency domain is shown in the figure below

2. Draw a time domain graph

1. Read the wav file
(pyaudio.PyAudio)
2. Convert the waveform data to an array
(numpy.frombuffer)
3. Construct the abscissa
(numpy.arange)
4. Draw the graph
(matplotlib)

import wave
import pyaudio
import pylab
import numpy as np
import matplotlib.pyplot as plt

def get_framerate(wavefile):
    '''
        输入文件路径，获取帧率
    '''
    wf=wave.open(wavfile,"rb")#打开wav
    p = pyaudio.PyAudio()#创建PyAudio对象
    params = wf.getparams()#参数获取
    nchannels, sampwidth, framerate, nframes = params[:4]
    return framerate

def get_nframes(wavefile):
    '''
        输入文件路径，获取帧数
    '''
    wf=wave.open(wavfile,"rb")#打开wav
    p = pyaudio.PyAudio()#创建PyAudio对象
    params = wf.getparams()#参数获取
    nchannels, sampwidth, framerate, nframes = params[:4]
    return nframes
    
def get_wavedata(wavfile):
    '''
        输入文件路径，获取处理好的 N-2 左右声部数组
    '''
    #####1.读入wave文件
    wf=wave.open(wavfile,"rb")#打开wav
    p = pyaudio.PyAudio()#创建PyAudio对象
    params = wf.getparams()#参数获取
    nchannels, sampwidth, framerate, nframes = params[:4]
    stream = p.open(format=p.get_format_from_width(sampwidth),
                    channels=nchannels, 
                    rate=framerate,
                    output=True)#创建输出流
    #读取完整的帧数据到str_data中，这是一个string类型的数据
    str_data = wf.readframes(nframes)
    wf.close()#关闭wave

    #####2.将波形数据转换为数组
    # N-1 一维数组，右声道接着左声道
    wave_data = numpy.frombuffer(str_data, dtype=numpy.short)
    #2-N N维数组
    wave_data.shape = -1,2
    #将数组转置为 N-2 目标数组
    wave_data = wave_data.T
    return wave_data

def plot_timedomain(wavfile):
    '''
        画出时域图
    '''
    wave_data=get_wavedata(wavfile) #获取处理好的wave数据
    framerate=get_framerate(wavfile) #获取帧率
    nframes=get_nframes(wavfile) #获取帧数
    
    #####3.构建横坐标
    time = numpy.arange(0,nframes)*(1.0/framerate)

    #####4.画图
    pylab.figure(figsize=(40,10))
    pylab.subplot(211)
    pylab.plot(time, wave_data[0]) #第一幅图：左声道
    pylab.subplot(212)
    pylab.plot(time, wave_data[1], c="g") #第二幅图：右声道
    pylab.xlabel("time (seconds)")
    pylab.show()
    return None

plot_timedomain(wavfile=wavfile)

3. Draw the frequency domain graph

1. Take the required part for Fourier transform and get the amplitude *
(numppy.fft.rfft)
2. Calculate the frequency column as the x-axis
(np.linspace)
3. Draw a graph
(matplotlib)

def plot_freqdomain(start,fft_size,wavfile):
    '''
        画出频域图
    '''
    waveData=get_wavedata(wavfile) #获取wave数据
    framerate=get_framerate(wavfile) #获取帧率数据
    
    #### 1.取出所需部分进行傅里叶变换，并得到幅值
    # rfft，对称保留一半，结果为 fft_size/2-1 维复数数组
    fft_y1 = np.fft.rfft(waveData[0][start:start+fft_size-1])/fft_size #左声部
    fft_y2 = np.fft.rfft(waveData[1][start:start+fft_size-1])/fft_size #右声部
   
    #### 2.计算频域图x值
    #最小值为0Hz，最大值一般设为采样频率的一半
    freqs = np.linspace(0, framerate/2, fft_size/2)

    #### 3.画图
    plt.figure(figsize=(20,10))
    pylab.subplot(211)
    plt.plot(freqs, np.abs(fft_y1))
    pylab.xlabel("frequence(Hz)")
    pylab.subplot(212)
    plt.plot(freqs, np.abs(fft_y2),c='g')
    pylab.xlabel("frequence(Hz)")
    plt.show()

plot_freqdomain(10000,4000,wavfile)