def generate_dataset(n_measures,
tempo=Tempo(120),
scale=GenericScale('C', [0, 2, 3, 5, 7, 8, 10]),
sampling_info=SamplingInfo(44100)):
signature = Signature(4, 4)
n_notes_per_measure = 4
bass_instrument = make_bass_instrument(sampling_info)
lead_instrument = make_lead_instrument(sampling_info)
chord_instrument = make_accomp_instrument(sampling_info)
chord_track = generate_chord_track(scale, tempo, signature, n_measures)
y_chord = chord_instrument.generate_track(chord_track)
lead_track = generate_lead_track(scale, tempo, signature, n_measures,
n_notes_per_measure)
y_lead = lead_instrument.generate_track(lead_track)
bass_track = generate_bass_track(scale, tempo, signature, n_measures)
y_bass = bass_instrument.generate_track(bass_track)
mix = mixdown([y_bass, y_chord, y_lead])
return [bass_track, chord_track, lead_track], [y_bass, y_chord,
y_lead], mix
def test_generation():
sampling_info = SamplingInfo(88200)
instrument = Instrument(AliasingSquareOscillator(sampling_info))
vj = vader_jacob()
y = instrument.generate_track(vj)
def lead():
lead = generate_dataset(n_measures=4,
tempo=Tempo(120),
scale=GenericScale('C', [0, 2, 3, 5, 7, 8, 10]),
sampling_info=SamplingInfo(44100))[1][2]
lead = np.concatenate((lead, np.zeros(shape=44100)), axis=0)
return lead
def test_blit():
sampling_info = SamplingInfo(44100)
x = np.arange(0, 1., 1/44100.) * 440 * 2 * np.pi
y = signalproc.blit(sampling_info, x, 440, 0, -1)
plt.plot(x, y)
# plt.show()
pass
def test_bl_square():
sampling_info = SamplingInfo(44100)
x = np.arange(0, 1., 1/44100.) * 1440 * 2 * np.pi
pos = signalproc.blit(sampling_info, x, 1440, 0, -1)
neg = signalproc.blit(sampling_info, x, 1440, np.pi, -1)
y = np.cumsum(pos - neg)
# y = pos
plt.plot(x, y)
# plt.show()
# play_array(y)
pass
def test_apply_filter():
"""Testing low- and high-pass filters
Note that the hard-coded test values are not exact and are merely checking
if the function yield reasonable behaviour.
"""
amp = 1
sampling_info = SamplingInfo(44100)
osc = SineOscillator(sampling_info)
data = osc.generate(amplitude=amp, duration=1, frequency=440, phase=0)
y = apply_filter(data, sampling_info, 440, 5, 'lowpass')
assert np.max(y) <= 0.55 * amp
assert np.max(y) >= 0.45 * amp
y = apply_filter(data, sampling_info, 220, 5, 'lowpass')
assert np.max(y) <= 0.05 * amp
y = apply_filter(data, sampling_info, 220, 5, 'highpass')
assert np.max(y) >= 0.95 * amp
pass
def generate_synthetic_data(batch_size, fragment_length, sampling_rate):
sampling_info = SamplingInfo(sampling_rate)
all_roots = scales.chromatic_scale('C')
# Generate in all keys
roots = [n.get_symbol() for n in all_roots.generate(0, 1)]
print(roots)
with Pool(8) as pool:
datasets_train = pool.map(
partial(__generate_dataset_for_root, sampling_info=sampling_info),
roots)
datasets_test = pool.map(
partial(__generate_dataset_for_root, sampling_info=sampling_info),
roots)
x_train, y_train = __process_dataset(datasets_train, batch_size,
fragment_length)
x_test, y_test = __process_dataset(datasets_test, batch_size,
fragment_length)
return x_train, y_train, x_test, y_test
def test_additive_osc():
additive = AdditiveOscillator(SamplingInfo(44100), [1, 0.5, 0.25, 0.125])
fx = additive.generate(0.5, 1, 440, 0)
def sampling_info():
return SamplingInfo(44100)