#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Mimic a middle C on the piano.

@author: khe
"""
import numpy as np
from scipy.io import wavfile
import matplotlib.pyplot as plt
plt.style.use('seaborn-dark')
import utils

# Get middle C frequency
note_freqs = utils.get_piano_notes()
frequency = note_freqs['C4']

# Pure sine wave
sine_wave = utils.get_sine_wave(frequency, duration=2, amplitude=2048)
wavfile.write('data/pure_c.wav', rate=44100, data=sine_wave.astype(np.int16))

# Load data from wav file
sample_rate, middle_c = wavfile.read('data/piano_c.wav')

# Plot sound wave
plt.plot(middle_c[500:2500])
plt.xlabel('Time')
plt.ylabel('Amplitude')
plt.title('Sound Wave of Middle C on Piano')
plt.grid()
plt.savefig('data/piano_sound_wave.jpg')

# FFT
t = np.arange(middle_c.shape[0])
freq = np.fft.fftfreq(t.shape[-1])*sample_rate
sp = np.fft.fft(middle_c) 

# Plot spectrum
plt.plot(freq, abs(sp.real))
plt.xlabel('Frequency (Hz)')
plt.ylabel('Amplitude')
plt.title('Spectrum of Middle C Recording on Piano')
plt.xlim((0, 2000))
plt.grid()
plt.savefig('data/spectrum.jpg')

# Get positive frequency
idx = np.where(freq > 0)[0]
freq = freq[idx]
sp = sp[idx]

# Get dominant frequencies
sort = np.argsort(-abs(sp.real))[:100]
dom_freq = freq[sort]

# Round and calculate amplitude ratio
freq_ratio = np.round(dom_freq/frequency)
unique_freq_ratio = np.unique(freq_ratio)
amp_ratio = abs(sp.real[sort]/np.sum(sp.real[sort]))
factor = np.zeros((int(unique_freq_ratio[-1]), ))
for i in range(factor.shape[0]):
    idx = np.where(freq_ratio==i+1)[0]
    factor[i] = np.sum(amp_ratio[idx])
factor = factor/np.sum(factor)

# Construct harmonic series
note = utils.apply_overtones(frequency, duration=2.5, factor=factor)

# Apply smooth ADSR weights
weights = utils.get_adsr_weights(frequency, duration=2.5, length=[0.05, 0.25, 0.55, 0.15],
                           decay=[0.075,0.02,0.005,0.1], sustain_level=0.1)

# Write to file
data = note*weights
data = data*(4096/np.max(data)) # Adjusting the Amplitude 
wavfile.write('data/synthetic_c.wav', sample_rate, data.astype(np.int16))
cfff9eab7554efa4d9a726a7e3cd1a9d63ec17e2
<mask token>

<mask token>
plt.style.use('seaborn-dark')
<mask token>
wavfile.write('data/pure_c.wav', rate=44100, data=sine_wave.astype(np.int16))
<mask token>
plt.plot(middle_c[500:2500])
plt.xlabel('Time')
plt.ylabel('Amplitude')
plt.title('Sound Wave of Middle C on Piano')
plt.grid()
plt.savefig('data/piano_sound_wave.jpg')
<mask token>
plt.plot(freq, abs(sp.real))
plt.xlabel('Frequency (Hz)')
plt.ylabel('Amplitude')
plt.title('Spectrum of Middle C Recording on Piano')
plt.xlim((0, 2000))
plt.grid()
plt.savefig('data/spectrum.jpg')
<mask token>
for i in range(factor.shape[0]):
    idx = np.where(freq_ratio == i + 1)[0]
    factor[i] = np.sum(amp_ratio[idx])
<mask token>
wavfile.write('data/synthetic_c.wav', sample_rate, data.astype(np.int16))

<mask token>
plt.style.use('seaborn-dark')
<mask token>
note_freqs = utils.get_piano_notes()
frequency = note_freqs['C4']
sine_wave = utils.get_sine_wave(frequency, duration=2, amplitude=2048)
wavfile.write('data/pure_c.wav', rate=44100, data=sine_wave.astype(np.int16))
sample_rate, middle_c = wavfile.read('data/piano_c.wav')
plt.plot(middle_c[500:2500])
plt.xlabel('Time')
plt.ylabel('Amplitude')
plt.title('Sound Wave of Middle C on Piano')
plt.grid()
plt.savefig('data/piano_sound_wave.jpg')
t = np.arange(middle_c.shape[0])
freq = np.fft.fftfreq(t.shape[-1]) * sample_rate
sp = np.fft.fft(middle_c)
plt.plot(freq, abs(sp.real))
plt.xlabel('Frequency (Hz)')
plt.ylabel('Amplitude')
plt.title('Spectrum of Middle C Recording on Piano')
plt.xlim((0, 2000))
plt.grid()
plt.savefig('data/spectrum.jpg')
idx = np.where(freq > 0)[0]
freq = freq[idx]
sp = sp[idx]
sort = np.argsort(-abs(sp.real))[:100]
dom_freq = freq[sort]
freq_ratio = np.round(dom_freq / frequency)
unique_freq_ratio = np.unique(freq_ratio)
amp_ratio = abs(sp.real[sort] / np.sum(sp.real[sort]))
factor = np.zeros((int(unique_freq_ratio[-1]),))
for i in range(factor.shape[0]):
    idx = np.where(freq_ratio == i + 1)[0]
    factor[i] = np.sum(amp_ratio[idx])
factor = factor / np.sum(factor)
note = utils.apply_overtones(frequency, duration=2.5, factor=factor)
weights = utils.get_adsr_weights(frequency, duration=2.5, length=[0.05, 
    0.25, 0.55, 0.15], decay=[0.075, 0.02, 0.005, 0.1], sustain_level=0.1)
data = note * weights
data = data * (4096 / np.max(data))
wavfile.write('data/synthetic_c.wav', sample_rate, data.astype(np.int16))

<mask token>
import numpy as np
from scipy.io import wavfile
import matplotlib.pyplot as plt
plt.style.use('seaborn-dark')
import utils
note_freqs = utils.get_piano_notes()
frequency = note_freqs['C4']
sine_wave = utils.get_sine_wave(frequency, duration=2, amplitude=2048)
wavfile.write('data/pure_c.wav', rate=44100, data=sine_wave.astype(np.int16))
sample_rate, middle_c = wavfile.read('data/piano_c.wav')
plt.plot(middle_c[500:2500])
plt.xlabel('Time')
plt.ylabel('Amplitude')
plt.title('Sound Wave of Middle C on Piano')
plt.grid()
plt.savefig('data/piano_sound_wave.jpg')
t = np.arange(middle_c.shape[0])
freq = np.fft.fftfreq(t.shape[-1]) * sample_rate
sp = np.fft.fft(middle_c)
plt.plot(freq, abs(sp.real))
plt.xlabel('Frequency (Hz)')
plt.ylabel('Amplitude')
plt.title('Spectrum of Middle C Recording on Piano')
plt.xlim((0, 2000))
plt.grid()
plt.savefig('data/spectrum.jpg')
idx = np.where(freq > 0)[0]
freq = freq[idx]
sp = sp[idx]
sort = np.argsort(-abs(sp.real))[:100]
dom_freq = freq[sort]
freq_ratio = np.round(dom_freq / frequency)
unique_freq_ratio = np.unique(freq_ratio)
amp_ratio = abs(sp.real[sort] / np.sum(sp.real[sort]))
factor = np.zeros((int(unique_freq_ratio[-1]),))
for i in range(factor.shape[0]):
    idx = np.where(freq_ratio == i + 1)[0]
    factor[i] = np.sum(amp_ratio[idx])
factor = factor / np.sum(factor)
note = utils.apply_overtones(frequency, duration=2.5, factor=factor)
weights = utils.get_adsr_weights(frequency, duration=2.5, length=[0.05, 
    0.25, 0.55, 0.15], decay=[0.075, 0.02, 0.005, 0.1], sustain_level=0.1)
data = note * weights
data = data * (4096 / np.max(data))
wavfile.write('data/synthetic_c.wav', sample_rate, data.astype(np.int16))

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Mimic a middle C on the piano.

@author: khe
"""
import numpy as np
from scipy.io import wavfile
import matplotlib.pyplot as plt
plt.style.use('seaborn-dark')
import utils

# Get middle C frequency
note_freqs = utils.get_piano_notes()
frequency = note_freqs['C4']

# Pure sine wave
sine_wave = utils.get_sine_wave(frequency, duration=2, amplitude=2048)
wavfile.write('data/pure_c.wav', rate=44100, data=sine_wave.astype(np.int16))

# Load data from wav file
sample_rate, middle_c = wavfile.read('data/piano_c.wav')

# Plot sound wave
plt.plot(middle_c[500:2500])
plt.xlabel('Time')
plt.ylabel('Amplitude')
plt.title('Sound Wave of Middle C on Piano')
plt.grid()
plt.savefig('data/piano_sound_wave.jpg')

# FFT
t = np.arange(middle_c.shape[0])
freq = np.fft.fftfreq(t.shape[-1])*sample_rate
sp = np.fft.fft(middle_c) 

# Plot spectrum
plt.plot(freq, abs(sp.real))
plt.xlabel('Frequency (Hz)')
plt.ylabel('Amplitude')
plt.title('Spectrum of Middle C Recording on Piano')
plt.xlim((0, 2000))
plt.grid()
plt.savefig('data/spectrum.jpg')

# Get positive frequency
idx = np.where(freq > 0)[0]
freq = freq[idx]
sp = sp[idx]

# Get dominant frequencies
sort = np.argsort(-abs(sp.real))[:100]
dom_freq = freq[sort]

# Round and calculate amplitude ratio
freq_ratio = np.round(dom_freq/frequency)
unique_freq_ratio = np.unique(freq_ratio)
amp_ratio = abs(sp.real[sort]/np.sum(sp.real[sort]))
factor = np.zeros((int(unique_freq_ratio[-1]), ))
for i in range(factor.shape[0]):
    idx = np.where(freq_ratio==i+1)[0]
    factor[i] = np.sum(amp_ratio[idx])
factor = factor/np.sum(factor)

# Construct harmonic series
note = utils.apply_overtones(frequency, duration=2.5, factor=factor)

# Apply smooth ADSR weights
weights = utils.get_adsr_weights(frequency, duration=2.5, length=[0.05, 0.25, 0.55, 0.15],
                           decay=[0.075,0.02,0.005,0.1], sustain_level=0.1)

# Write to file
data = note*weights
data = data*(4096/np.max(data)) # Adjusting the Amplitude 
wavfile.write('data/synthetic_c.wav', sample_rate, data.astype(np.int16))