def generate(self, n_noise_samples=1):
"""Generate noise samples.
The type of the noise that will be generated, and the size of the noise array are defined by the argument given
to the constructor.
:param n_noise_samples: The number of noise samples to be generated.
:return: an np.array with the specified noise
"""
n = n_noise_samples * self.noise_size[0] * self.noise_size[1]
s = concat([n_noise_samples], list(self.noise_size))
if self.noise_type == 'simplistic':
return np.random.uniform(0, 1, size=concat([n_noise_samples], list(self.noise_size)))
elif self.noise_type.lower() in {'gaussian', 'white', 'normal'}:
return np.reshape(white(n), s)
elif self.noise_type.lower() == 'pink':
return np.reshape(pink(n), s)
elif self.noise_type.lower() == 'blue':
return np.reshape(blue(n), s)
elif self.noise_type.lower() == 'brown':
return np.reshape(brown(n), s)
elif self.noise_type.lower() == 'violet':
return np.reshape(violet(n), s)
else:
print("WARNING: noise type " + self.noise_type + " not defined. Returning 0")
return np.reshape(np.zeros(n), s)
def __init__(self, size=1000, sample_rate=16000, transform=None):
self.size = size
self.sample_rate = sample_rate
self.transform = None if transform is None else Compose(
transform.transforms[1:])
n = 1000000
compute_mag_spectrogram = ComputeMagSpectrogram()
self.n_fft = compute_mag_spectrogram.n_fft
def compute_spectrogram(sample):
clipped_sample = np.clip(sample / 5, -1, 1).astype(
np.float32) # amplitude is around (-0.7, 0.7)
return compute_mag_spectrogram({
'text': '',
'samples': clipped_sample,
'sample_rate': sample_rate
})['input']
self.noises = [
compute_spectrogram(white(n)),
compute_spectrogram(pink(n)),
compute_spectrogram(violet(n))
]
trees = pickle.load(f)
for id in test_ids:
print("\n -- loading", id)
audio_path = os.path.join(args.root, 'test_data', str(id) + '.wav')
# print( ' ', audio_path)
y, sr = load(audio_path, normalization=True, channels_first=False)
y = y.mean(1)
y = F.pad(y, (frame_size // 2, frame_size // 2))
y = y[:len(y) // hopsize * hopsize].unfold(0, frame_size, hopsize)
if args.snr is None:
pass
else:
noise = torch.Tensor(pink(frame_size))
data_pow = y.pow(2).mean(1)
scaler = _noise_scaler(data_pow, args.snr)
noise = scaler[:, None] * noise
y += noise
print(" contaminated with pink noise at SNR = " + str(args.snr))
y = y / (y.norm(dim=1, keepdim=True) + epsilon)
label = torch.zeros(y.size(0), 88)
label_tree = trees[id]
for i in range(y.size(0)):
for note in label_tree[i * hopsize]:
label[i, note.data[1] - 21] = 1
if y.shape[0] > label.shape[0]:
def Pink_noise(N):
x = gen.pink(N)
return x
def test_power_density(self):
fs = 44100
samples = 44100 * 10
_, L = octaves(pink(samples), fs, density=True)
change = np.diff(L).mean().round(0)
assert (change == -3.)
def test_power(self):
fs = 44100
samples = 44100 * 10
_, L = octaves(pink(samples), fs)
change = np.diff(L).mean().round(0)
assert (change == 0.)
def test_length(self):
N = 1000
assert (len(pink(N)) == N)
import numpy as np """ create signal """ # manually Ns, fs = 44100, 44100. dt = 1 / fs sigf = 1000. sigA = np.sqrt(2) sigx = np.linspace(0, Ns-1, num=Ns)*dt sigy = sigA * np.sin(2*np.pi*sigf*sigx) + 0.2*np.sin(2*np.pi*2*sigf*sigx) # using acoustics import acoustics.generator as acg sigz1 = acg.white(Ns) sigz2 = acg.pink(Ns) """ descriptors """ import acoustics.descriptors as acd print "reference SPL:", acd.REFERENCE_PRESSURE print "equivalent SPL:",acd.equivalent_sound_pressure_level(sigy) print "peak SPL:", acd.peak_sound_pressure(sigy) """ IEC 61672 """ import acoustics.standards as acs print "third octave center frequency vector:", acs.iec_61672_1_2013.NOMINAL_OCTAVE_CENTER_FREQUENCIES print "reference frequency:", acs.iec_61672_1_2013.REFERENCE_FREQUENCY print "slow time constant:", acs.iec_61672_1_2013.SLOW
def test_power_density(self):
fs = 44100
samples = 44100 * 10
_, L = octaves(pink(samples), fs, density=True);
change = np.diff(L).mean().round(0)
assert(change==-3.)
def test_power(self):
fs = 44100
samples = 44100 * 10
_, L = octaves(pink(samples), fs);
change = np.diff(L).mean().round(0)
assert(change==0.)
def test_length(self):
N = 1000
assert(len(pink(N))==N)