用python 绘制语谱图
1.步骤：
1）导入相关模块
2）读入音频并获取音频参数
3）将音频转化为可处理形式（注意读入的是字符串格式，需要转换成int或short型）
代码如下：
-
import numpy
as np
-
import matplotlib.pyplot
as plt
-
import os
-
import wave
-
-
#读入音频。
-
path =
"E:\SpeechWarehouse\zmkm"
-
name =
'zmkm0.wav'
-
#我音频的路径为E:\SpeechWarehouse\zmkm\zmkm0.wav
-
filename = os.path.join(path, name)
-
-
# 打开语音文件。
-
f = wave.open(filename,
'rb')
-
# 得到语音参数
-
params = f.getparams()
-
nchannels, sampwidth, framerate,nframes = params[:
4]
-
-
# 将字符串格式的数据转成int型
-
strData = f.readframes(nframes)
-
waveData = np.fromstring(strData,dtype=np.short)
-
# 归一化
-
waveData = waveData *
1.0/max(abs(waveData))
-
#将音频信号规整乘每行一路通道信号的格式，即该矩阵一行为一个通道的采样点，共nchannels行
-
waveData = np.reshape(waveData,[nframes,nchannels]).T
# .T 表示转置
-
f.close()
#关闭文件
其中getparams方法介绍如下：
getnchannels() -- returns number of audio channels (1 for mono, 2 for stereo)
getsampwidth() -- returns sample width in bytes
getframerate() -- returns sampling frequency
getnframes() -- returns number of audio frames
getparams() -- returns a namedtuple consisting of all of the above in the above order
稍微翻译一下：
nchannels:音频通道数(the number of audio channels),getnchannels()
sampwidth:每个音频样本的字节数(the number of bytes per audio sample),getsampwidth()
framerate:采样率(the sampling frequency),getframerate()
nframes:音频采样点数(the number of audio frames),getnframes()
4）绘制时域波形：
4.1）计算时间：t = n/fs
4.2）绘图
-
'''绘制语音波形'''
-
time = np.arange(
0,nframes) * (
1.0 / framerate)
-
time= np.reshape(time,[nframes,
1]).T
-
plt.plot(time[
0,:nframes],waveData[
0,:nframes],c=
"b")
-
plt.xlabel(
"time(seconds)")
-
plt.ylabel(
"amplitude")
-
plt.title(
"Original wave")
-
plt.show()
时域波形如图：
5）绘制语谱图：
5.1)求出帧长，一般取20~30ms
N = t*fs 每帧点数等于每帧时间乘以采样率
帧叠点数，一般取每帧点数的1/3~1/2
且FFT点数等于每帧点数（即不补零）
5.2)绘制语谱图，利用specgram()方法
-
#绘制频谱
-
print(
"plotting spectrogram...")
-
framelength =
0.025
#帧长20~30ms
-
framesize = framelength*framerate
#每帧点数 N = t*fs,通常情况下值为256或512,要与NFFT相等\
-
#而NFFT最好取2的整数次方,即framesize最好取的整数次方
-
-
#找到与当前framesize最接近的2的正整数次方
-
nfftdict = {}
-
lists = [
32,
64,
128,
256,
512,
1024]
-
for i
in lists:
-
nfftdict[i] = abs(framesize - i)
-
sortlist = sorted(nfftdict.items(), key=
lambda x: x[
1])
#按与当前framesize差值升序排列
-
framesize = int(sortlist[
0][
0])
#取最接近当前framesize的那个2的正整数次方值为新的framesize
-
-
NFFT = framesize
#NFFT必须与时域的点数framsize相等，即不补零的FFT
-
overlapSize =
1.0/
3 * framesize
#重叠部分采样点数overlapSize约为每帧点数的1/3~1/2
-
overlapSize = int(round(overlapSize))
#取整
-
spectrum,freqs,ts,fig = plt.specgram(waveData[
0],NFFT = NFFT,Fs =framerate,window=np.hanning(M = framesize),noverlap=overlapSize,mode=
'default',scale_by_freq=
True,sides=
'default',scale=
'dB',xextent=
None)
#绘制频谱图
-
plt.ylabel(
'Frequency')
-
plt.xlabel(
'Time(s)')
-
plt.title(
'Spectrogram')
-
plt.show()
specgram()方法概述，详细信息见官网
matplotlib.pyplot.specgram(x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None, noverlap=None, cmap=None, xextent=None, pad_to=None, sides=None, scale_by_freq=None, mode=None, scale=None, vmin=None, vmax=None, *, data=None, **kwargs)
#参数：
x : 信号，一维arry或deqyence
NFFT：fft点数，默认256.不应该用于的零填充，最好为2的整数次方
Fs：采样率，默认2
Fc：信号x的中心频率，默认为0，用于移动图像，
window : 窗函数，长度必须等于NFFT（帧长），默认为汉宁窗
window_hanning(), window_none(), numpy.blackman(), numpy.hamming(), numpy.bartlett(), scipy.signal(), scipy.signal.get_window(), etc.
sides : {'default', 'onesided', 'twosided'}单边频谱或双边谱
Default gives the default behavior, which returns one-sided for real data and both for complex data.
'onesided' forces the return of a one-sided spectrum,
while 'twosided' forces two-sided.
pad_to : 执行FFT时填充数据的点数，可以与NFFT不同（补零，不会增加频谱分辨率，可以减轻栅栏效应，默认为None,即等于NFFT）
scale_by_freq : bool, optional是否按密度缩放频率，MATLAB默认为真
Specifies whether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^-1.
This allows for integration over the returned frequency values. The default is True for MATLAB compatibility.
mode : 使用什么样的频谱，默认为PSD谱（功率谱）{'default', 'psd', 'magnitude', 'angle', 'phase'}
'complex' returns the complex-valued frequency spectrum.
'magnitude' returns the magnitude spectrum.
'angle' returns the phase spectrum without unwrapping.
'phase' returns the phase spectrum with unwrapping.
noverlap : 帧叠点数，默认为128
scale : {'default', 'linear', 'dB'}频谱纵坐标单位,默认为dB
xextent : None or (xmin, xmax)图像x轴范围
cmap :A matplotlib.colors.Colormap instance; if , use default determined by rc
detrend : {'default', 'constant', 'mean', 'linear', 'none'}
The function applied to each segment before fft-ing, designed to remove the mean or linear trend.
Unlike in MATLAB, where the detrend parameter is a vector, in matplotlib is it a function.
The mlab module defines detrend_none(), detrend_mean(), and detrend_linear(), but you can use a custom function as well.
You can also use a string to choose one of the functions. 'default', 'constant', and 'mean' call detrend_mean(). 'linear' calls detrend_linear(). 'none' calls detrend_none()
#返回：
spectrum:频谱矩阵
freqs：频谱图每行对应的频率
ts：频谱图每列对应的时间
fig ：图像
结果如图：
2.完整代码
-
import numpy
as np
-
import matplotlib.pyplot
as plt
-
import os
-
import wave
-
-
#读入音频。
-
path =
"E:\SpeechWarehouse\zmkm"
-
name =
'zmkm0.wav'
-
#我音频的路径为E:\SpeechWarehouse\zmkm\zmkm0.wav
-
filename = os.path.join(path, name)
-
-
# 打开语音文件。
-
f = wave.open(filename,
'rb')
-
# 得到语音参数
-
params = f.getparams()
-
nchannels, sampwidth, framerate,nframes = params[:
4]
-
#---------------------------------------------------------------#
-
# 将字符串格式的数据转成int型
-
print(
"reading wav file......")
-
strData = f.readframes(nframes)
-
waveData = np.fromstring(strData,dtype=np.short)
-
# 归一化
-
waveData = waveData *
1.0/max(abs(waveData))
-
#将音频信号规整乘每行一路通道信号的格式，即该矩阵一行为一个通道的采样点，共nchannels行
-
waveData = np.reshape(waveData,[nframes,nchannels]).T
# .T 表示转置
-
f.close()
#关闭文件
-
print(
"file is closed!")
-
#----------------------------------------------------------------#
-
'''绘制语音波形'''
-
print(
"plotting signal wave...")
-
time = np.arange(
0,nframes) * (
1.0 / framerate)
#计算时间
-
time= np.reshape(time,[nframes,
1]).T
-
plt.plot(time[
0,:nframes],waveData[
0,:nframes],c=
"b")
-
plt.xlabel(
"time")
-
plt.ylabel(
"amplitude")
-
plt.title(
"Original wave")
-
plt.show()
-
-
#--------------------------------------------------------------#
-
'''
-
绘制频谱
-
1.求出帧长、帧叠点数。且FFT点数等于每帧点数（即不补零）
-
2.绘制语谱图
-
'''
-
print(
"plotting spectrogram...")
-
framelength =
0.025
#帧长20~30ms
-
framesize = framelength*framerate
#每帧点数 N = t*fs,通常情况下值为256或512,要与NFFT相等\
-
#而NFFT最好取2的整数次方,即framesize最好取的整数次方
-
-
#找到与当前framesize最接近的2的正整数次方
-
nfftdict = {}
-
lists = [
32,
64,
128,
256,
512,
1024]
-
for i
in lists:
-
nfftdict[i] = abs(framesize - i)
-
sortlist = sorted(nfftdict.items(), key=
lambda x: x[
1])
#按与当前framesize差值升序排列
-
framesize = int(sortlist[
0][
0])
#取最接近当前framesize的那个2的正整数次方值为新的framesize
-
-
NFFT = framesize
#NFFT必须与时域的点数framsize相等，即不补零的FFT
-
overlapSize =
1.0/
3 * framesize
#重叠部分采样点数overlapSize约为每帧点数的1/3~1/2
-
overlapSize = int(round(overlapSize))
#取整
-
print(
"帧长为{},帧叠为{},傅里叶变换点数为{}".format(framesize,overlapSize,NFFT))
-
spectrum,freqs,ts,fig = plt.specgram(waveData[
0],NFFT = NFFT,Fs =framerate,window=np.hanning(M = framesize),noverlap=overlapSize,mode=
'default',scale_by_freq=
True,sides=
'default',scale=
'dB',xextent=
None)
#绘制频谱图
-
-
plt.ylabel(
'Frequency')
-
plt.xlabel(
'Time')
-
plt.title(
"Spectrogram")
-
plt.show()
扫一扫
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
