python code

import numpy as np
import wave
import struct

def find_nearest(array,value):
    idx = (np.abs(array-value)).argmin()
    return array[idx]

############################## Initialize ##################################


# Some Useful Variables
window_size = 2000    # Size of window to be used for detecting silence
beta = 1   # Silence detection parameter
max_notes = 100    # Maximum number of notes in file, for efficiency
sampling_freq = 44100	# Sampling frequency of audio signal
threshold = 200
array = [27.5000 ,29.1352,30.8677,32.7032,34.6478,36.7081,38.8909,41.2034,43.6535,46.2493,48.9994,51.9131,55.0000,58.2705,61.7354,65.4064,69.2957,73.4162,
77.7817,82.4069 ,87.3071,92.4986,97.9989,103.8262,110.0000,116.5409,123.4708,130.8128,138.5913,146.8324,155.5635,164.8138,174.6141,184.9972,195.9977, 207.6523, 220.0000,
233.0819, 246.9417, 261.6256,277.1826, 293.6648,311.1270,329.6276,349.2282,369.9944, 391.9954,415.3047, 440.0000, 466.1638,493.8833,523.2511, 554.3653,
587.3295,622.2540,659.2551, 698.4565,739.9888, 783.9909, 830.6094,880.0000,932.3275,987.7666,1046.5023,1108.7305,1174.6591, 1244.5079,1318.5102,1396.9129,
1479.9777,1567.9817,1661.2188, 1760.0000,1864.6550, 1975.5332,2093.0045,2217.4610,2349.3181,2489.0159,2637.0205,2793.8259,2959.9554,3135.9635,3322.4376,
3520.0000,3729.3101,3951.0664,4186.0090]

notes = ['A0','A#0','B0','C1','C#1','D1','D#1','E1','F1' ,'F#1','G1','G#1','A1','A#1', 'B1','C2','C#2','D2', 'D#2','E2','F2', 'F#2','G2', 'G#2','A2','A#2','B2',
         'C3','C#3','D3','D#3','E3','F3','F#3','G3','G#3','A3','A#3','B3','C4','C#4','D4','D#4','E4','F4','F#4','G4','G#4','A4','A#4','B4','C5','C#5','D5','D#5',
         'E5','F5','F#5','G5','G#5','A5','A#5','B5','C6','C#6','D6','D#6','E6','F6','F#6','G6','G#6','A6','A#6','B6','C7','C#7','D7','D#7','E7','F7','F#7','G7',
         'G#7','A7','A#7','B7','C8']

Identified_Notes = []

############################## Read Audio File #############################
print ('\n\nReading Audio File...')

sound_file = wave.open('Audio_2.wav', 'r')
file_length = sound_file.getnframes()

sound = np.zeros(file_length)
mean_square = []
sound_square = np.zeros(file_length)
for i in range(file_length):
    data = sound_file.readframes(1)
    data = struct.unpack("<h", data)
    sound[i] = int(data[0])

sound = np.divide(sound, float(2**15))	# Normalize data in range -1 to 1


######################### DETECTING SCILENCE ##################################

sound_square = np.square(sound)
frequency = []
dft = []
i = 0
j = 0
k = 0
# traversing sound_square array with a fixed window_size
while(i<=len(sound_square)-window_size):
	s = 0.0
	j = 0
	while(j<=window_size):
		s = s + sound_square[i+j]
		j = j + 1
        # detecting the silence waves
	if s < threshold:
		if(i-k>window_size*4):
			dft = np.array(dft) # applying fourier transform function
			dft = np.fft.fft(sound[k:i])
			dft = np.argsort(dft)

			if(dft[0]>dft[-1] and dft[1]>dft[-1]):
				i_max = dft[-1]
			elif(dft[1]>dft[0] and dft[-1]>dft[0]):
				i_max = dft[0]
			else :
				i_max = dft[1]
                        # claculating frequency
			frequency.append((i_max*sampling_freq)/(i-k))
			dft = []
			k = i+1
	i = i + window_size

print('length',len(frequency))
print("frequency")
avg=0;
for i in frequency :
	print(i)
	avg+=avg
	idx = (np.abs(array-i)).argmin()
	Identified_Notes.append(notes[idx])
print(Identified_Notes)