This post is for the people who love Signal Processing.Well,currently Matlab is one of the most used software by the signal processing community,but enough of Matlab,really!!! These days almost everyone knows how to use Matlab.
Python on the other hand is another very powerful language which also can be used for signal/image processing .
Well here’s how to get started with signal processing in python
1)You are gonna need some python libraries such as numpy,scipy,matplotlib and pylab.These are available for free.Ubuntu and Debian users can download them by typing this on the terminal
sudo apt-get install python-numpy python-scipy python-matplotlib
2)Numpy is the numerical library of python which includes modules for 2D arrays(or lists),fourier transform ,dft etc.Scipy is the scientific library used for importing .wav file in this case.Matplotlib is python’s 2D plotting library .
In this post I am gonna start with a simple code,
Computing the Spectogram of an audio signal.
A spectrogram, or sonogram, is a visual representation of the spectrum of frequencies in a sound. Spectrograms are sometimes called spectral waterfalls, voiceprints, or voicegrams.
Procedure for finding the spectogram of a signal is as follows :
- Read the signal from a .wav file into a 2D numpy array.
- Divide the signal in to overlapping frames,keeping each frame size say 25ms ,and overlapping window size as 10ms
- Take the short time fourier transform of each windowed frame
- Compute the power spectrum of each frame,i.e. the square of the absolute value of the DFT of each frame.
Explanation of the python code:
- import numpy
import matplotlib.pyplot as plt
import scipy.io.wavfile #This library is used for reading the .wav file
- [fs,signal]=scipy.io.wavfile.read(‘w1.wav’) #input wav file ,change here
# fs=sampling frequency,signal is the numpy 2D array where the data of the wav file is written
- length=len(signal) # the length of the wav file.This gives the number of samples ,not the length in time
window_hop_length=0.01 #10ms change here
window_size=0.025 #25 ms,change here
nfft_length=framesize #length of DFT ,change here
print “number of frames are =”,number_of_frames
- frames=numpy.ndarray((number_of_frames,framesize)) # This declares a 2D matrix,with rows equal to the number of frames,and columns equal to the framesize or the length of each DTF
for k in range(0,number_of_frames):
for i in range(0,framesize):
- fft_matrix=numpy.ndarray((number_of_frames,framesize)) #declares another 2d matrix to store the DFT of each windowed frame
abs_fft_matrix=numpy.ndarray((number_of_frames,framesize)) #declares another 2D Matrix to store the power spectrum
- for k in range(0,number_of_frames):
fft_matrix[k]=numpy.fft.fft(frames[k]) #computes the DFT
abs_fft_matrix[k]=abs(fft_matrix[k])*abs(fft_matrix[k])/(max(abs(fft_matrix[k]))) # computes the power spectrum
- t=range(len(abs_fft_matrix)) #This code segment simply plots the power spectrum obtained above