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musicalNoteDetector.py
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musicalNoteDetector.py
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import sys
import random
import math
import os
import pyaudio
from scipy import signal
from random import *
import numpy as np
from scipy.signal import blackmanharris, fftconvolve
from numpy import argmax, sqrt, mean, diff, log
import time
import wave
def find(condition):
res, = np.nonzero(np.ravel(condition))
return res
def build_default_tuner_range():
return {65.41:'Do2',
69.30:'Do2#',
73.42:'Re2',
77.78:'Mi2b',
82.41:'Mi2',
87.31:'Fa2',
92.50:'Fa2#',
98.00:'Sol2',
103.80:'Sol2#',
110.00:'La2',
116.50:'Si2b',
123.50:'Si2',
130.80:'Do3',
138.60:'Do3#',
146.80:'Re3',
155.60:'Mi3b',
164.80:'Mi3',
174.60:'Fa3',
185.00:'Fa3#',
196.00:'Sol3',
207.70:'Sol3#',
220.00:'La3',
233.10:'Si3b',
246.90:'Si3',
261.60:'Do4',
277.20:'Do4#',
293.70:'Re4',
311.10:'Mi4b',
329.60:'Mi4',
349.20:'Fa4',
370.00:'Fa4#',
392.00:'Sol4',
415.30:'Sol4#',
440.00:'La4',
466.20:'Si4b',
493.90:'Si4',
523.30:'Do5',
554.40:'Do5#',
587.30:'Re5',
622.30:'Mi5b',
659.30:'Mi5',
698.50:'Fa5',
740.00:'Fa5#',
784.00:'Sol5',
830.60:'Sol5#',
880.00:'La5',
932.30:'Si5b',
987.80:'Si5',
1047.00:'Do6',
1109.0:'Do6#',
1175.0:'Re6',
1245.0:'Mi6b',
1319.0:'Mi6',
1397.0:'Fa6',
1480.0:'Fa6#',
1568.0:'Sol6',
1661.0:'Sol6#',
1760.0:'La6',
1865.0:'Si6b',
1976.0:'Si6',
2093.0:'Do7'
}
RATE=48000
BUFFERSIZE=3072
FORMAT = pyaudio.paInt16
soundgate = 19
tunerNotes = build_default_tuner_range()
frequencies = np.array(sorted( tunerNotes.keys()) )
def callback(in_data, frame_count, time_info, status):
# raw_data_signal = np.fromstring( in_data,dtype= np.int16 )
raw_data_signal = np.frombuffer( in_data,dtype= np.int16 )
signal_level = round(abs(loudness(raw_data_signal)),2) #### find the volume from the audio
try:
inputnote = round(freq_from_autocorr(raw_data_signal,RATE),2) #### find the freq from the audio
except:
inputnote = 0
if inputnote > frequencies[len(tunerNotes)-1]:
return ( raw_data_signal, pyaudio.paContinue )
if inputnote < frequencies[0]:
return ( raw_data_signal, pyaudio.paContinue )
if signal_level > soundgate:
return ( raw_data_signal, pyaudio.paContinue )
targetnote = closest_value_index(frequencies, round(inputnote, 2))
print(tunerNotes[frequencies[targetnote]])
return ( in_data, pyaudio.paContinue )
# See https://github.com/endolith/waveform-analyzer/blob/master/frequency_estimator.py
def freq_from_autocorr(raw_data_signal, fs):
corr = fftconvolve(raw_data_signal, raw_data_signal[::-1], mode='full')
corr = corr[len(corr)//2:]
d = diff(corr)
start = find(d > 0)[0]
peak = argmax(corr[start:]) + start
px, py = parabolic(corr, peak)
return fs / px
# See https://github.com/endolith/waveform-analyzer/blob/master/frequency_estimator.py
def parabolic(f, x):
xv = 1/2. * (f[x-1] - f[x+1]) / (f[x-1] - 2 * f[x] + f[x+1]) + x
yv = f[x] - 1/4. * (f[x-1] - f[x+1]) * (xv - x)
return (xv, yv)
def loudness(chunk):
data = np.array(chunk, dtype=float) / 32768.0
ms = math.sqrt(np.sum(data ** 2.0) / len(data))
if ms < 10e-8: ms = 10e-8
return 10.0 * math.log(ms, 10.0)
def find_nearest(array, value):
index = (np.abs(array - value)).argmin()
return array[index]
def closest_value_index(array, guessValue):
# Find closest element in the array, value wise
closestValue = find_nearest(array, guessValue)
# Find indices of closestValue
indexArray = np.where(array == closestValue)
# Numpys 'where' returns a 2D array with the element index as the value
return indexArray[0][0]
def read_from_mic():
p = pyaudio.PyAudio()
stream = p.open(
format = FORMAT,
channels=1,
rate = RATE,
output=False,
input=True,
frames_per_buffer = BUFFERSIZE,
stream_callback = callback )
stream.start_stream()
while stream.is_active():
time.sleep( 0.1 )
stream.stop_stream()
stream.close()
def read_from_wav(file):
CHUNK = 1024
wf = wave.open(file, 'rb')
data = wf.readframes(CHUNK)
while data != b'':
data = wf.readframes(CHUNK)
callback(data, 0, 0, 0)
if __name__ == "__main__":
read_from_wav("GuitarMod.wav")
# read_from_mic()