def real_stft(self):
snd = self.sound_cls.random()
windowed = choice(snd.windowed)
windowed = AudioSamples(
windowed,
audio_sample_rate(windowed.dimensions[0].samples_per_second))
return self._stft(windowed)
def fft(x, axis=-1, padding_samples=0):
"""
Apply an FFT along the given dimension, and with the specified amount of
zero-padding
Args:
x (ArrayWithUnits): an :class:`~zounds.core.ArrayWithUnits` instance
which has one or more :class:`~zounds.timeseries.TimeDimension`
axes
axis (int): The axis along which the fft should be applied
padding_samples (int): The number of padding zeros to apply along
axis before performing the FFT
"""
if padding_samples > 0:
padded = np.concatenate(
[x, np.zeros((len(x), padding_samples), dtype=x.dtype)], axis=axis)
else:
padded = x
transformed = np.fft.rfft(padded, axis=axis, norm='ortho')
sr = audio_sample_rate(int(Seconds(1) / x.dimensions[axis].frequency))
scale = LinearScale.from_sample_rate(sr, transformed.shape[-1])
new_dimensions = list(x.dimensions)
new_dimensions[axis] = FrequencyDimension(scale)
return ArrayWithUnits(transformed, new_dimensions)
def frequency_decomposition(x, sizes):
sizes = sorted(sizes)
if x.ndim == 1:
end = x.dimensions[0].end
x = ArrayWithUnits(x[None, ...],
[TimeDimension(end, end), x.dimensions[0]])
original_size = x.shape[-1]
time_dimension = x.dimensions[-1]
samplerate = audio_sample_rate(time_dimension.samples_per_second)
data = x.copy()
bands = []
frequency_bands = []
start_hz = 0
for size in sizes:
if size != original_size:
s = resample(data, size, axis=-1)
else:
s = data.copy()
bands.append(s)
data -= resample(s, original_size, axis=-1)
stop_hz = samplerate.nyquist * (size / original_size)
frequency_bands.append(FrequencyBand(start_hz, stop_hz))
start_hz = stop_hz
scale = ExplicitScale(frequency_bands)
return FrequencyAdaptive(bands,
scale=scale,
time_dimension=x.dimensions[0])
def real_stft(self):
snd = self.sound_cls.random()
windowed = choice(snd.windowed)
windowed = AudioSamples(
windowed,
audio_sample_rate(windowed.dimensions[0].samples_per_second))
return self._stft(windowed)
def _get_samples(self):
raw_samples = self._sf.read(self._chunk_size_samples)
sr = audio_sample_rate(self._sf.samplerate)
samples = AudioSamples(raw_samples, sr)
if self._sum_to_mono:
return samples.mono
return samples
def _process(self, data):
transformed = self._process_raw(data)
sr = audio_sample_rate(data.dimensions[1].samples_per_second)
scale = LinearScale.from_sample_rate(sr, transformed.shape[1])
yield ArrayWithUnits(
transformed, [data.dimensions[0], FrequencyDimension(scale)])
def _process(self, data):
raw = self._process_raw(data)
sr = audio_sample_rate(
int(data.shape[1] / data.dimensions[0].duration_in_seconds))
scale = LinearScale.from_sample_rate(
sr, data.shape[1], always_even=self.scale_always_even)
yield ArrayWithUnits(
raw, [data.dimensions[0], FrequencyDimension(scale)])
def _process(self, data):
transformed = self._process_raw(data)
sr = audio_sample_rate(data.dimensions[1].samples_per_second)
scale = LinearScale.from_sample_rate(sr, transformed.shape[1])
yield ArrayWithUnits(
transformed,
[data.dimensions[0], FrequencyDimension(scale)])
def _process(self, data):
raw = self._process_raw(data)
sr = audio_sample_rate(
int(data.shape[1] / data.dimensions[0].duration_in_seconds))
scale = LinearScale.from_sample_rate(
sr, data.shape[1], always_even=self.scale_always_even)
yield ArrayWithUnits(
raw,
[data.dimensions[0], FrequencyDimension(scale)])
def _process(self, data):
transformed = dct(data, norm='ortho', axis=self._axis)
sr = audio_sample_rate(
int(data.shape[1] / data.dimensions[0].duration_in_seconds))
scale = LinearScale.from_sample_rate(
sr, transformed.shape[-1], always_even=self.scale_always_even)
yield ArrayWithUnits(
transformed, [data.dimensions[0], FrequencyDimension(scale)])
def _process(self, data):
transformed = dct(data, norm='ortho', axis=self._axis)
sr = audio_sample_rate(
int(data.shape[1] / data.dimensions[0].duration_in_seconds))
scale = LinearScale.from_sample_rate(
sr, transformed.shape[-1], always_even=self.scale_always_even)
yield ArrayWithUnits(
transformed,
[data.dimensions[0], FrequencyDimension(scale)])
def __getitem__(self, timeslice):
sr = audio_sample_rate(self.samplerate)
if timeslice == slice(None):
self._sf.seek(0)
return AudioSamples(self._sf.read(len(self._sf)), sr)
start_sample = int(timeslice.start / self._freq)
n_samples = self._n_samples(timeslice.duration)
self._sf.seek(start_sample)
return AudioSamples(self._sf.read(n_samples), sr)
def __getitem__(self, timeslice):
sr = audio_sample_rate(self.samplerate)
if timeslice == slice(None):
self._sf.seek(0)
return AudioSamples(self._sf.read(len(self._sf)), sr)
start_sample = int(timeslice.start / self._freq)
n_samples = self._n_samples(timeslice.duration)
self._sf.seek(start_sample)
return AudioSamples(self._sf.read(n_samples), sr)
def __call__(self, flo):
raw = super(AudioSamplesDecoder, self).__call__(flo)
samplerate = audio_sample_rate(raw.dimensions[0].samples_per_second)
return AudioSamples(raw, samplerate)
