def __init__(self, chord, first_single, second_single, third_single):
self.audio_reader = Audio_Reader()
self.chord = self.audio_reader.read_sample(chord)
self.first_single = self.audio_reader.read_sample(first_single)
self.second_single = self.audio_reader.read_sample(second_single)
self.third_single = self.audio_reader.read_sample(third_single)
self.chord_file = chord
self.first_single_file = first_single
self.second_single_file = second_single
self.third_single_file = third_single
def __init__(self, chord, first_single, second_single, third_single):
self.audio_reader = Audio_Reader()
self.chord = self.audio_reader.read_sample(chord)
self.first_single = self.audio_reader.read_sample(first_single)
self.second_single = self.audio_reader.read_sample(second_single)
self.third_single = self.audio_reader.read_sample(third_single)
self.chord_file = chord
self.first_single_file = first_single
self.second_single_file = second_single
self.third_single_file = third_single
class Threshold_Finder(object):
def __init__(self, chord, first_single, second_single, third_single):
self.audio_reader = Audio_Reader()
self.chord = self.audio_reader.read_sample(chord)
self.first_single = self.audio_reader.read_sample(first_single)
self.second_single = self.audio_reader.read_sample(second_single)
self.third_single = self.audio_reader.read_sample(third_single)
self.chord_file = chord
self.first_single_file = first_single
self.second_single_file = second_single
self.third_single_file = third_single
def get_FFT(self, sample):
# TODO: check if the fft_lengths are the same and if not decide what to do with it...
fft_length = int(
self.audio_reader.get_duration(self.chord_file) *
self.audio_reader.get_framerate(self.chord_file))
if is_Prime(fft_length):
FFT = numpy.fft.fft(sample, n=fft_length - 1)
else:
FFT = numpy.fft.fft(sample, n=fft_length)
return FFT
def find_least_squares(self):
fft_chord = self.get_FFT(self.chord)
fft_first = self.get_FFT(self.first_single)
fft_second = self.get_FFT(self.second_single)
fft_third = self.get_FFT(self.third_single)
chord_array = []
first_array = []
second_array = []
third_array = []
for i in range(len(fft_chord)):
chord_array.append(fft_chord[i].real)
chord_array.append(fft_chord[i].imag)
first_array.append(fft_first[i].real)
first_array.append(fft_first[i].imag)
second_array.append(fft_second[i].real)
second_array.append(fft_second[i].imag)
third_array.append(fft_third[i].real)
third_array.append(fft_third[i].imag)
A = numpy.array([first_array, second_array, third_array]).T
b = chord_array
# x: coefficients for each transform for single notes, rnorm: sum of squares of magnitudes of the noise.
x, rnorm = scipy.optimize.nnls(A, b)
average_noise_power = rnorm**2 / len(fft_chord)
return (x, rnorm, average_noise_power)
def get_FFT_of_noise(self, x, rnorm):
sum_of_singles = x[0] * self.get_FFT(
self.first_single) + x[1] * self.get_FFT(
self.second_single) + x[2] * self.get_FFT(self.third_single)
fft = scipy.absolute(self.get_FFT(self.chord) - sum_of_singles)
return fft
def get_sum_of_squares(self, fft):
sum_of_squares = 0.0
for i in range(len(fft)):
sum_of_squares += scipy.absolute(fft[i]) * sscipy.absolute(fft[i])
return sum_of_squares
def plot_power_spectrum(self, fft):
T = int(600)
pylab.figure('Power spectrum')
pylab.plot(scipy.absolute(fft[:T]) * scipy.absolute(fft[:T]), )
pylab.xlabel('Frequency [Hz]')
pylab.ylabel('Power spectrum []')
pylab.show()
class Threshold_Finder(object):
def __init__(self, chord, first_single, second_single, third_single):
self.audio_reader = Audio_Reader()
self.chord = self.audio_reader.read_sample(chord)
self.first_single = self.audio_reader.read_sample(first_single)
self.second_single = self.audio_reader.read_sample(second_single)
self.third_single = self.audio_reader.read_sample(third_single)
self.chord_file = chord
self.first_single_file = first_single
self.second_single_file = second_single
self.third_single_file = third_single
def get_FFT(self, sample):
# TODO: check if the fft_lengths are the same and if not decide what to do with it...
fft_length = int(self.audio_reader.get_duration(self.chord_file) * self.audio_reader.get_framerate(self.chord_file))
if is_Prime(fft_length):
FFT = numpy.fft.fft(sample, n=fft_length-1)
else:
FFT = numpy.fft.fft(sample, n=fft_length)
return FFT
def find_least_squares(self):
fft_chord = self.get_FFT(self.chord)
fft_first = self.get_FFT(self.first_single)
fft_second = self.get_FFT(self.second_single)
fft_third = self.get_FFT(self.third_single)
chord_array = []
first_array = []
second_array = []
third_array = []
for i in range(len(fft_chord)):
chord_array.append(fft_chord[i].real)
chord_array.append(fft_chord[i].imag)
first_array.append(fft_first[i].real)
first_array.append(fft_first[i].imag)
second_array.append(fft_second[i].real)
second_array.append(fft_second[i].imag)
third_array.append(fft_third[i].real)
third_array.append(fft_third[i].imag)
A = numpy.array([first_array, second_array, third_array]).T
b = chord_array
# x: coefficients for each transform for single notes, rnorm: sum of squares of magnitudes of the noise.
x, rnorm = scipy.optimize.nnls(A, b)
average_noise_power = rnorm ** 2 / len(fft_chord)
return (x, rnorm, average_noise_power)
def get_FFT_of_noise(self, x, rnorm):
sum_of_singles = x[0] * self.get_FFT(self.first_single) + x[1] * self.get_FFT(self.second_single) + x[2] * self.get_FFT(self.third_single)
fft = scipy.absolute(self.get_FFT(self.chord) - sum_of_singles)
return fft
def get_sum_of_squares(self, fft):
sum_of_squares = 0.0
for i in range(len(fft)):
sum_of_squares += scipy.absolute(fft[i]) * sscipy.absolute(fft[i])
return sum_of_squares
def plot_power_spectrum(self, fft):
T = int(600)
pylab.figure('Power spectrum')
pylab.plot(scipy.absolute(fft[:T]) * scipy.absolute(fft[:T]),)
pylab.xlabel('Frequency [Hz]')
pylab.ylabel('Power spectrum []')
pylab.show()
