Audio signal processing

audio_signal_processing.py

#required libraries
import urllib
import scipy.io.wavfile
import pydub

#a temp folder for downloads
temp_folder="/Users/home/Desktop/"

#spotify mp3 sample file
web_file="http://p.scdn.co/mp3-preview/35b4ce45af06203992a86fa729d17b1c1f93cac5"

#download file
urllib.urlretrieve(web_file,temp_folder+"file.mp3")
#read mp3 file
mp3 = pydub.AudioSegment.from_mp3(temp_folder+"file.mp3")
#convert to wav
mp3.export(temp_folder+"file.wav", format="wav")
#read wav file
rate,audData=scipy.io.wavfile.read(temp_folder+"file.wav")

#the sample rate is the number of bits of infomration recorded per second
print(rate)
print(audData)

#wav bit type the amount of information recorded in each bit often 8, 16 or 32 bit
audData.dtype

#wav length
audData.shape[0] / rate

#wav number of channels mono/stereo 
audData.shape[1]
#if stereo grab both channels
channel1=audData[:,0] #left 
channel2=audData[:,1] #right

import numpy as np

#Energy of music
np.sum(channel1.astype(float)**2)
#this can be infinite and depends on the length of the music of the loudness often talk about power
#power - energy per unit of time
1.0/(2*(channel1.size)+1)*np.sum(channel1.astype(float)**2)/rate

#save wav file
scipy.io.wavfile.write(temp_folder+"file2.wav", rate, audData)
#save a file at half and double speed
scipy.io.wavfile.write(temp_folder+"file2.wav", rate/2, audData)
scipy.io.wavfile.write(temp_folder+"file2.wav", rate*2, audData)

#save a single channel
scipy.io.wavfile.write(temp_folder+"file2.wav", rate, channel1)

#averaging the channels damages the music
mono=np.sum(audData.astype(float), axis=1)/2
scipy.io.wavfile.write(temp_folder+"file2.wav", rate, mono)



import matplotlib.pyplot as plt

time = np.arange(0, float(audData.shape[0]), 1) / rate 

#plot amplitude (or loudness) over time
plt.figure(1)
plt.subplot(211)
plt.plot(time, channel1, linewidth=0.02, alpha=0.7, color='#ff7f00')
plt.xlabel('Time (s)')
plt.ylabel('Amplitude')
plt.subplot(212)
plt.plot(time, channel2, linewidth=0.02, alpha=0.7, color='#ff7f00')
plt.xlabel('Time (s)')
plt.ylabel('Amplitude')
plt.savefig(temp_folder+'ampiltude.png', bbox_inches='tight')




#Frequency (pitch) over time

#a fourier transform breaks the sound wave into series of waves that make up the main sound wave
#each of these waves will have its own amplitude (volume) and frequency. The frequency is the length over which the wave repeats itself. this is known as the pitch of the sound

from numpy import fft as fft

fourier=fft.fft(channel1)

plt.figure(1, figsize=(8,6))
plt.plot(fourier, color='#ff7f00')
plt.xlabel('k')
plt.ylabel('Amplitude')
plt.savefig(temp_folder+'fft.png', bbox_inches='tight')




#the fourier is symetrical due to the real and imaginary soultion. only interested in first real solution
n = len(channel1) 
fourier = fourier[0:(n/2)]

# scale by the number of points so that the magnitude does not depend on the length 
fourier = fourier / float(n) 
                 
#calculate the frequency at each point in Hz
freqArray = np.arange(0, (n/2), 1.0) * (rate*1.0/n);

plt.figure(1, figsize=(8,6))
plt.plot(freqArray/1000, 10*np.log10(fourier), color='#ff7f00', linewidth=0.02)
plt.xlabel('Frequency (kHz)')
plt.ylabel('Power (dB)')
plt.savefig(temp_folder+'frequencies.png', bbox_inches='tight')





#plot spectogram
#the function calculates many fft's over NFFT sized blocks of data
#increasing NFFT gives you a more detail across the spectrum range but decreases the samples per second
#the sampling rate used determines the frequency range seen always 0 to rate/2

plt.figure(2, figsize=(8,6))
plt.subplot(211)
Pxx, freqs, bins, im = plt.specgram(channel1, Fs=rate, NFFT=1024, cmap=plt.get_cmap('autumn_r'))
cbar=plt.colorbar(im)
plt.xlabel('Time (s)')
plt.ylabel('Frequency (Hz)')
cbar.set_label('Intensity dB')
plt.subplot(212)
Pxx, freqs, bins, im = plt.specgram(channel2, Fs=rate, NFFT=1024, cmap=plt.get_cmap('autumn_r'))
cbar=plt.colorbar(im)
plt.xlabel('Time (s)')
plt.ylabel('Frequency (Hz)')
cbar.set_label('Intensity (dB)')
#plt.show()
plt.savefig(temp_folder+'spectogram.png', bbox_inches='tight')




#Larger Window Size value increases frequency resolution
#Smaller Window Size value increases time resolution
#Specify a Frequency Range to be calculated for using the Goertzel function
#Specify which axis to put frequency


Pxx, freqs, timebins, im = plt.specgram(channel2, Fs=rate, NFFT=1024, noverlap=0, cmap=plt.get_cmap('autumn_r'))

channel1.shape
Pxx.shape 
freqs.shape
timebins.shape
np.min(freqs)
np.max(freqs)
np.min(timebins)
np.max(timebins)


np.where(freqs==10034.47265625)
MHZ10=Pxx[233,:]
plt.figure(figsize=(8,6))
plt.plot(timebins, MHZ10, color='#ff7f00')
plt.savefig(temp_folder+'MHZ10.png', bbox_inches='tight')

What's the point of lines 150 to 157? You are not doing anything with the values it returns if I understood correctly.