_id stringlengths 2 7 | title stringlengths 1 88 | partition stringclasses 3
values | text stringlengths 75 19.8k | language stringclasses 1
value | meta_information dict |
|---|---|---|---|---|---|
q267900 | A_weighting | test | def A_weighting(frequencies, min_db=-80.0): # pylint: disable=invalid-name
'''Compute the A-weighting of a set of frequencies.
Parameters
----------
frequencies : scalar or np.ndarray [shape=(n,)]
One or more frequencies (in Hz)
min_db : float [scalar] or None
Clip weights belo... | python | {
"resource": ""
} |
q267901 | times_like | test | def times_like(X, sr=22050, hop_length=512, n_fft=None, axis=-1):
"""Return an array of time values to match the time axis from a feature matrix.
Parameters
----------
X : np.ndarray or scalar
- If ndarray, X is a feature matrix, e.g. STFT, chromagram, or mel spectrogram.
- If scalar, X... | python | {
"resource": ""
} |
q267902 | samples_like | test | def samples_like(X, hop_length=512, n_fft=None, axis=-1):
"""Return an array of sample indices to match the time axis from a feature matrix.
Parameters
----------
X : np.ndarray or scalar
- If ndarray, X is a feature matrix, e.g. STFT, chromagram, or mel spectrogram.
- If scalar, X repr... | python | {
"resource": ""
} |
q267903 | hybrid_cqt | test | def hybrid_cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,
bins_per_octave=12, tuning=0.0, filter_scale=1,
norm=1, sparsity=0.01, window='hann', scale=True,
pad_mode='reflect', res_type=None):
'''Compute the hybrid constant-Q transform of an audio signal.
Her... | python | {
"resource": ""
} |
q267904 | pseudo_cqt | test | def pseudo_cqt(y, sr=22050, hop_length=512, fmin=None, n_bins=84,
bins_per_octave=12, tuning=0.0, filter_scale=1,
norm=1, sparsity=0.01, window='hann', scale=True,
pad_mode='reflect'):
'''Compute the pseudo constant-Q transform of an audio signal.
This uses a single... | python | {
"resource": ""
} |
q267905 | icqt | test | def icqt(C, sr=22050, hop_length=512, fmin=None, bins_per_octave=12,
tuning=0.0, filter_scale=1, norm=1, sparsity=0.01, window='hann',
scale=True, length=None, amin=util.Deprecated(), res_type='fft'):
'''Compute the inverse constant-Q transform.
Given a constant-Q transform representation `C`... | python | {
"resource": ""
} |
q267906 | __cqt_filter_fft | test | def __cqt_filter_fft(sr, fmin, n_bins, bins_per_octave, tuning,
filter_scale, norm, sparsity, hop_length=None,
window='hann'):
'''Generate the frequency domain constant-Q filter basis.'''
basis, lengths = filters.constant_q(sr,
f... | python | {
"resource": ""
} |
q267907 | __trim_stack | test | def __trim_stack(cqt_resp, n_bins):
'''Helper function to trim and stack a collection of CQT responses'''
# cleanup any framing errors at the boundaries
max_col = min(x.shape[1] for x in cqt_resp)
cqt_resp = np.vstack([x[:, :max_col] for x in cqt_resp][::-1])
# Finally, clip out any bottom freque... | python | {
"resource": ""
} |
q267908 | __cqt_response | test | def __cqt_response(y, n_fft, hop_length, fft_basis, mode):
'''Compute the filter response with a target STFT hop.'''
# Compute the STFT matrix
D = stft(y, n_fft=n_fft, hop_length=hop_length,
window='ones',
pad_mode=mode)
# And filter response energy
return fft_basis.dot(D... | python | {
"resource": ""
} |
q267909 | __early_downsample_count | test | def __early_downsample_count(nyquist, filter_cutoff, hop_length, n_octaves):
'''Compute the number of early downsampling operations'''
downsample_count1 = max(0, int(np.ceil(np.log2(audio.BW_FASTEST * nyquist /
filter_cutoff)) - 1) - 1)
num_twos = __num_t... | python | {
"resource": ""
} |
q267910 | __early_downsample | test | def __early_downsample(y, sr, hop_length, res_type, n_octaves,
nyquist, filter_cutoff, scale):
'''Perform early downsampling on an audio signal, if it applies.'''
downsample_count = __early_downsample_count(nyquist, filter_cutoff,
hop_lengt... | python | {
"resource": ""
} |
q267911 | __dtw_calc_accu_cost | test | def __dtw_calc_accu_cost(C, D, D_steps, step_sizes_sigma,
weights_mul, weights_add, max_0, max_1): # pragma: no cover
'''Calculate the accumulated cost matrix D.
Use dynamic programming to calculate the accumulated costs.
Parameters
----------
C : np.ndarray [shape=(N, M)... | python | {
"resource": ""
} |
q267912 | __dtw_backtracking | test | def __dtw_backtracking(D_steps, step_sizes_sigma): # pragma: no cover
'''Backtrack optimal warping path.
Uses the saved step sizes from the cost accumulation
step to backtrack the index pairs for an optimal
warping path.
Parameters
----------
D_steps : np.ndarray [shape=(N, M)]
S... | python | {
"resource": ""
} |
q267913 | _viterbi | test | def _viterbi(log_prob, log_trans, log_p_init, state, value, ptr): # pragma: no cover
'''Core Viterbi algorithm.
This is intended for internal use only.
Parameters
----------
log_prob : np.ndarray [shape=(T, m)]
`log_prob[t, s]` is the conditional log-likelihood
log P[X = X(t) | St... | python | {
"resource": ""
} |
q267914 | viterbi_discriminative | test | def viterbi_discriminative(prob, transition, p_state=None, p_init=None, return_logp=False):
'''Viterbi decoding from discriminative state predictions.
Given a sequence of conditional state predictions `prob[s, t]`,
indicating the conditional likelihood of state `s` given the
observation at time `t`, an... | python | {
"resource": ""
} |
q267915 | transition_uniform | test | def transition_uniform(n_states):
'''Construct a uniform transition matrix over `n_states`.
Parameters
----------
n_states : int > 0
The number of states
Returns
-------
transition : np.ndarray [shape=(n_states, n_states)]
`transition[i, j] = 1./n_states`
Examples
... | python | {
"resource": ""
} |
q267916 | transition_loop | test | def transition_loop(n_states, prob):
'''Construct a self-loop transition matrix over `n_states`.
The transition matrix will have the following properties:
- `transition[i, i] = p` for all i
- `transition[i, j] = (1 - p) / (n_states - 1)` for all `j != i`
This type of transition matrix is ... | python | {
"resource": ""
} |
q267917 | transition_cycle | test | def transition_cycle(n_states, prob):
'''Construct a cyclic transition matrix over `n_states`.
The transition matrix will have the following properties:
- `transition[i, i] = p`
- `transition[i, i + 1] = (1 - p)`
This type of transition matrix is appropriate for state spaces
with cycl... | python | {
"resource": ""
} |
q267918 | transition_local | test | def transition_local(n_states, width, window='triangle', wrap=False):
'''Construct a localized transition matrix.
The transition matrix will have the following properties:
- `transition[i, j] = 0` if `|i - j| > width`
- `transition[i, i]` is maximal
- `transition[i, i - width//2 : i + ... | python | {
"resource": ""
} |
q267919 | onset_detect | test | def onset_detect(y=None, sr=22050, onset_envelope=None, hop_length=512,
backtrack=False, energy=None,
units='frames', **kwargs):
"""Basic onset detector. Locate note onset events by picking peaks in an
onset strength envelope.
The `peak_pick` parameters were chosen by lar... | python | {
"resource": ""
} |
q267920 | onset_strength | test | def onset_strength(y=None, sr=22050, S=None, lag=1, max_size=1,
ref=None,
detrend=False, center=True,
feature=None, aggregate=None,
centering=None,
**kwargs):
"""Compute a spectral flux onset strength envelope.
Onset... | python | {
"resource": ""
} |
q267921 | onset_backtrack | test | def onset_backtrack(events, energy):
'''Backtrack detected onset events to the nearest preceding local
minimum of an energy function.
This function can be used to roll back the timing of detected onsets
from a detected peak amplitude to the preceding minimum.
This is most useful when using onsets ... | python | {
"resource": ""
} |
q267922 | onset_strength_multi | test | def onset_strength_multi(y=None, sr=22050, S=None, lag=1, max_size=1,
ref=None, detrend=False, center=True, feature=None,
aggregate=None, channels=None, **kwargs):
"""Compute a spectral flux onset strength envelope across multiple channels.
Onset strength for c... | python | {
"resource": ""
} |
q267923 | times_csv | test | def times_csv(path, times, annotations=None, delimiter=',', fmt='%0.3f'):
r"""Save time steps as in CSV format. This can be used to store the output
of a beat-tracker or segmentation algorithm.
If only `times` are provided, the file will contain each value
of `times` on a row::
times[0]\n
... | python | {
"resource": ""
} |
q267924 | write_wav | test | def write_wav(path, y, sr, norm=False):
"""Output a time series as a .wav file
Note: only mono or stereo, floating-point data is supported.
For more advanced and flexible output options, refer to
`soundfile`.
Parameters
----------
path : str
path to save the output wav file... | python | {
"resource": ""
} |
q267925 | cmap | test | def cmap(data, robust=True, cmap_seq='magma', cmap_bool='gray_r', cmap_div='coolwarm'):
'''Get a default colormap from the given data.
If the data is boolean, use a black and white colormap.
If the data has both positive and negative values,
use a diverging colormap.
Otherwise, use a sequential c... | python | {
"resource": ""
} |
q267926 | waveplot | test | def waveplot(y, sr=22050, max_points=5e4, x_axis='time', offset=0.0,
max_sr=1000, ax=None, **kwargs):
'''Plot the amplitude envelope of a waveform.
If `y` is monophonic, a filled curve is drawn between `[-abs(y), abs(y)]`.
If `y` is stereo, the curve is drawn between `[-abs(y[1]), abs(y[0])]`... | python | {
"resource": ""
} |
q267927 | __set_current_image | test | def __set_current_image(ax, img):
'''Helper to set the current image in pyplot mode.
If the provided `ax` is not `None`, then we assume that the user is using the object API.
In this case, the pyplot current image is not set.
'''
if ax is None:
import matplotlib.pyplot as plt
plt.s... | python | {
"resource": ""
} |
q267928 | __mesh_coords | test | def __mesh_coords(ax_type, coords, n, **kwargs):
'''Compute axis coordinates'''
if coords is not None:
if len(coords) < n:
raise ParameterError('Coordinate shape mismatch: '
'{}<{}'.format(len(coords), n))
return coords
coord_map = {'linear': __... | python | {
"resource": ""
} |
q267929 | __check_axes | test | def __check_axes(axes):
'''Check if "axes" is an instance of an axis object. If not, use `gca`.'''
if axes is None:
import matplotlib.pyplot as plt
axes = plt.gca()
elif not isinstance(axes, Axes):
raise ValueError("`axes` must be an instance of matplotlib.axes.Axes. "
... | python | {
"resource": ""
} |
q267930 | __scale_axes | test | def __scale_axes(axes, ax_type, which):
'''Set the axis scaling'''
kwargs = dict()
if which == 'x':
thresh = 'linthreshx'
base = 'basex'
scale = 'linscalex'
scaler = axes.set_xscale
limit = axes.set_xlim
else:
thresh = 'linthreshy'
base = 'basey'
... | python | {
"resource": ""
} |
q267931 | __coord_fft_hz | test | def __coord_fft_hz(n, sr=22050, **_kwargs):
'''Get the frequencies for FFT bins'''
n_fft = 2 * (n - 1)
# The following code centers the FFT bins at their frequencies
# and clips to the non-negative frequency range [0, nyquist]
basis = core.fft_frequencies(sr=sr, n_fft=n_fft)
fmax = basis[-1]
... | python | {
"resource": ""
} |
q267932 | __coord_mel_hz | test | def __coord_mel_hz(n, fmin=0, fmax=11025.0, **_kwargs):
'''Get the frequencies for Mel bins'''
if fmin is None:
fmin = 0
if fmax is None:
fmax = 11025.0
basis = core.mel_frequencies(n, fmin=fmin, fmax=fmax)
basis[1:] -= 0.5 * np.diff(basis)
basis = np.append(np.maximum(0, basis... | python | {
"resource": ""
} |
q267933 | __coord_cqt_hz | test | def __coord_cqt_hz(n, fmin=None, bins_per_octave=12, **_kwargs):
'''Get CQT bin frequencies'''
if fmin is None:
fmin = core.note_to_hz('C1')
# we drop by half a bin so that CQT bins are centered vertically
return core.cqt_frequencies(n+1,
fmin=fmin / 2.0**(0.5/bi... | python | {
"resource": ""
} |
q267934 | __coord_chroma | test | def __coord_chroma(n, bins_per_octave=12, **_kwargs):
'''Get chroma bin numbers'''
return np.linspace(0, (12.0 * n) / bins_per_octave, num=n+1, endpoint=True) | python | {
"resource": ""
} |
q267935 | __coord_time | test | def __coord_time(n, sr=22050, hop_length=512, **_kwargs):
'''Get time coordinates from frames'''
return core.frames_to_time(np.arange(n+1), sr=sr, hop_length=hop_length) | python | {
"resource": ""
} |
q267936 | estimate_tuning | test | def estimate_tuning(y=None, sr=22050, S=None, n_fft=2048,
resolution=0.01, bins_per_octave=12, **kwargs):
'''Estimate the tuning of an audio time series or spectrogram input.
Parameters
----------
y: np.ndarray [shape=(n,)] or None
audio signal
sr : number > 0 [scalar]
... | python | {
"resource": ""
} |
q267937 | piptrack | test | def piptrack(y=None, sr=22050, S=None, n_fft=2048, hop_length=None,
fmin=150.0, fmax=4000.0, threshold=0.1,
win_length=None, window='hann', center=True, pad_mode='reflect',
ref=None):
'''Pitch tracking on thresholded parabolically-interpolated STFT.
This implementation us... | python | {
"resource": ""
} |
q267938 | hpss | test | def hpss(y, **kwargs):
'''Decompose an audio time series into harmonic and percussive components.
This function automates the STFT->HPSS->ISTFT pipeline, and ensures that
the output waveforms have equal length to the input waveform `y`.
Parameters
----------
y : np.ndarray [shape=(n,)]
... | python | {
"resource": ""
} |
q267939 | harmonic | test | def harmonic(y, **kwargs):
'''Extract harmonic elements from an audio time-series.
Parameters
----------
y : np.ndarray [shape=(n,)]
audio time series
kwargs : additional keyword arguments.
See `librosa.decompose.hpss` for details.
Returns
-------
y_harmonic : np.ndarra... | python | {
"resource": ""
} |
q267940 | percussive | test | def percussive(y, **kwargs):
'''Extract percussive elements from an audio time-series.
Parameters
----------
y : np.ndarray [shape=(n,)]
audio time series
kwargs : additional keyword arguments.
See `librosa.decompose.hpss` for details.
Returns
-------
y_percussive : np.... | python | {
"resource": ""
} |
q267941 | time_stretch | test | def time_stretch(y, rate):
'''Time-stretch an audio series by a fixed rate.
Parameters
----------
y : np.ndarray [shape=(n,)]
audio time series
rate : float > 0 [scalar]
Stretch factor. If `rate > 1`, then the signal is sped up.
If `rate < 1`, then the signal is slowed d... | python | {
"resource": ""
} |
q267942 | pitch_shift | test | def pitch_shift(y, sr, n_steps, bins_per_octave=12, res_type='kaiser_best'):
'''Pitch-shift the waveform by `n_steps` half-steps.
Parameters
----------
y : np.ndarray [shape=(n,)]
audio time-series
sr : number > 0 [scalar]
audio sampling rate of `y`
n_steps : float [scalar]
... | python | {
"resource": ""
} |
q267943 | remix | test | def remix(y, intervals, align_zeros=True):
'''Remix an audio signal by re-ordering time intervals.
Parameters
----------
y : np.ndarray [shape=(t,) or (2, t)]
Audio time series
intervals : iterable of tuples (start, end)
An iterable (list-like or generator) where the `i`th item
... | python | {
"resource": ""
} |
q267944 | _signal_to_frame_nonsilent | test | def _signal_to_frame_nonsilent(y, frame_length=2048, hop_length=512, top_db=60,
ref=np.max):
'''Frame-wise non-silent indicator for audio input.
This is a helper function for `trim` and `split`.
Parameters
----------
y : np.ndarray, shape=(n,) or (2,n)
Audio ... | python | {
"resource": ""
} |
q267945 | trim | test | def trim(y, top_db=60, ref=np.max, frame_length=2048, hop_length=512):
'''Trim leading and trailing silence from an audio signal.
Parameters
----------
y : np.ndarray, shape=(n,) or (2,n)
Audio signal, can be mono or stereo
top_db : number > 0
The threshold (in decibels) below refe... | python | {
"resource": ""
} |
q267946 | split | test | def split(y, top_db=60, ref=np.max, frame_length=2048, hop_length=512):
'''Split an audio signal into non-silent intervals.
Parameters
----------
y : np.ndarray, shape=(n,) or (2, n)
An audio signal
top_db : number > 0
The threshold (in decibels) below reference to consider as
... | python | {
"resource": ""
} |
q267947 | phase_vocoder | test | def phase_vocoder(D, rate, hop_length=None):
"""Phase vocoder. Given an STFT matrix D, speed up by a factor of `rate`
Based on the implementation provided by [1]_.
.. [1] Ellis, D. P. W. "A phase vocoder in Matlab."
Columbia University, 2002.
http://www.ee.columbia.edu/~dpwe/resources/mat... | python | {
"resource": ""
} |
q267948 | amplitude_to_db | test | def amplitude_to_db(S, ref=1.0, amin=1e-5, top_db=80.0):
'''Convert an amplitude spectrogram to dB-scaled spectrogram.
This is equivalent to ``power_to_db(S**2)``, but is provided for convenience.
Parameters
----------
S : np.ndarray
input amplitude
ref : scalar or callable
If... | python | {
"resource": ""
} |
q267949 | _spectrogram | test | def _spectrogram(y=None, S=None, n_fft=2048, hop_length=512, power=1,
win_length=None, window='hann', center=True, pad_mode='reflect'):
'''Helper function to retrieve a magnitude spectrogram.
This is primarily used in feature extraction functions that can operate on
either audio time-serie... | python | {
"resource": ""
} |
q267950 | hpss_beats | test | def hpss_beats(input_file, output_csv):
'''HPSS beat tracking
:parameters:
- input_file : str
Path to input audio file (wav, mp3, m4a, flac, etc.)
- output_file : str
Path to save beat event timestamps as a CSV file
'''
# Load the file
print('Loading ', input_file... | python | {
"resource": ""
} |
q267951 | decompose | test | def decompose(S, n_components=None, transformer=None, sort=False, fit=True, **kwargs):
"""Decompose a feature matrix.
Given a spectrogram `S`, produce a decomposition into `components`
and `activations` such that `S ~= components.dot(activations)`.
By default, this is done with with non-negative matri... | python | {
"resource": ""
} |
q267952 | nn_filter | test | def nn_filter(S, rec=None, aggregate=None, axis=-1, **kwargs):
'''Filtering by nearest-neighbors.
Each data point (e.g, spectrogram column) is replaced
by aggregating its nearest neighbors in feature space.
This can be useful for de-noising a spectrogram or feature matrix.
The non-local means met... | python | {
"resource": ""
} |
q267953 | __nn_filter_helper | test | def __nn_filter_helper(R_data, R_indices, R_ptr, S, aggregate):
'''Nearest-neighbor filter helper function.
This is an internal function, not for use outside of the decompose module.
It applies the nearest-neighbor filter to S, assuming that the first index
corresponds to observations.
Parameters... | python | {
"resource": ""
} |
q267954 | mel | test | def mel(sr, n_fft, n_mels=128, fmin=0.0, fmax=None, htk=False,
norm=1, dtype=np.float32):
"""Create a Filterbank matrix to combine FFT bins into Mel-frequency bins
Parameters
----------
sr : number > 0 [scalar]
sampling rate of the incoming signal
n_fft : int > 0 [scalar... | python | {
"resource": ""
} |
q267955 | chroma | test | def chroma(sr, n_fft, n_chroma=12, A440=440.0, ctroct=5.0,
octwidth=2, norm=2, base_c=True, dtype=np.float32):
"""Create a Filterbank matrix to convert STFT to chroma
Parameters
----------
sr : number > 0 [scalar]
audio sampling rate
n_fft : int > 0 [scalar]
... | python | {
"resource": ""
} |
q267956 | __float_window | test | def __float_window(window_spec):
'''Decorator function for windows with fractional input.
This function guarantees that for fractional `x`, the following hold:
1. `__float_window(window_function)(x)` has length `np.ceil(x)`
2. all values from `np.floor(x)` are set to 0.
For integer-valued `x`, th... | python | {
"resource": ""
} |
q267957 | constant_q | test | def constant_q(sr, fmin=None, n_bins=84, bins_per_octave=12, tuning=0.0,
window='hann', filter_scale=1, pad_fft=True, norm=1,
dtype=np.complex64, **kwargs):
r'''Construct a constant-Q basis.
This uses the filter bank described by [1]_.
.. [1] McVicar, Matthew.
"A ... | python | {
"resource": ""
} |
q267958 | constant_q_lengths | test | def constant_q_lengths(sr, fmin, n_bins=84, bins_per_octave=12,
tuning=0.0, window='hann', filter_scale=1):
r'''Return length of each filter in a constant-Q basis.
Parameters
----------
sr : number > 0 [scalar]
Audio sampling rate
fmin : float > 0 [scalar]
Mi... | python | {
"resource": ""
} |
q267959 | cq_to_chroma | test | def cq_to_chroma(n_input, bins_per_octave=12, n_chroma=12,
fmin=None, window=None, base_c=True, dtype=np.float32):
'''Convert a Constant-Q basis to Chroma.
Parameters
----------
n_input : int > 0 [scalar]
Number of input components (CQT bins)
bins_per_octave : int > 0 [sc... | python | {
"resource": ""
} |
q267960 | window_bandwidth | test | def window_bandwidth(window, n=1000):
'''Get the equivalent noise bandwidth of a window function.
Parameters
----------
window : callable or string
A window function, or the name of a window function.
Examples:
- scipy.signal.hann
- 'boxcar'
n : int > 0
The... | python | {
"resource": ""
} |
q267961 | get_window | test | def get_window(window, Nx, fftbins=True):
'''Compute a window function.
This is a wrapper for `scipy.signal.get_window` that additionally
supports callable or pre-computed windows.
Parameters
----------
window : string, tuple, number, callable, or list-like
The window specification:
... | python | {
"resource": ""
} |
q267962 | _multirate_fb | test | def _multirate_fb(center_freqs=None, sample_rates=None, Q=25.0,
passband_ripple=1, stopband_attenuation=50, ftype='ellip', flayout='ba'):
r'''Helper function to construct a multirate filterbank.
A filter bank consists of multiple band-pass filters which divide the input signal
into subb... | python | {
"resource": ""
} |
q267963 | mr_frequencies | test | def mr_frequencies(tuning):
r'''Helper function for generating center frequency and sample rate pairs.
This function will return center frequency and corresponding sample rates
to obtain similar pitch filterbank settings as described in [1]_.
Instead of starting with MIDI pitch `A0`, we start with `C0`... | python | {
"resource": ""
} |
q267964 | __window_ss_fill | test | def __window_ss_fill(x, win_sq, n_frames, hop_length): # pragma: no cover
'''Helper function for window sum-square calculation.'''
n = len(x)
n_fft = len(win_sq)
for i in range(n_frames):
sample = i * hop_length
x[sample:min(n, sample + n_fft)] += win_sq[:max(0, min(n_fft, n - sample))... | python | {
"resource": ""
} |
q267965 | window_sumsquare | test | def window_sumsquare(window, n_frames, hop_length=512, win_length=None, n_fft=2048,
dtype=np.float32, norm=None):
'''
Compute the sum-square envelope of a window function at a given hop length.
This is used to estimate modulation effects induced by windowing observations
in short-t... | python | {
"resource": ""
} |
q267966 | diagonal_filter | test | def diagonal_filter(window, n, slope=1.0, angle=None, zero_mean=False):
'''Build a two-dimensional diagonal filter.
This is primarily used for smoothing recurrence or self-similarity matrices.
Parameters
----------
window : string, tuple, number, callable, or list-like
The window function ... | python | {
"resource": ""
} |
q267967 | spectral_centroid | test | def spectral_centroid(y=None, sr=22050, S=None, n_fft=2048, hop_length=512,
freq=None, win_length=None, window='hann', center=True,
pad_mode='reflect'):
'''Compute the spectral centroid.
Each frame of a magnitude spectrogram is normalized and treated as a
distrib... | python | {
"resource": ""
} |
q267968 | spectral_rolloff | test | def spectral_rolloff(y=None, sr=22050, S=None, n_fft=2048, hop_length=512,
win_length=None, window='hann', center=True, pad_mode='reflect',
freq=None, roll_percent=0.85):
'''Compute roll-off frequency.
The roll-off frequency is defined for each frame as the center freq... | python | {
"resource": ""
} |
q267969 | spectral_flatness | test | def spectral_flatness(y=None, S=None, n_fft=2048, hop_length=512,
win_length=None, window='hann', center=True, pad_mode='reflect',
amin=1e-10, power=2.0):
'''Compute spectral flatness
Spectral flatness (or tonality coefficient) is a measure to
quantify how much n... | python | {
"resource": ""
} |
q267970 | poly_features | test | def poly_features(y=None, sr=22050, S=None, n_fft=2048, hop_length=512,
win_length=None, window='hann', center=True, pad_mode='reflect',
order=1, freq=None):
'''Get coefficients of fitting an nth-order polynomial to the columns
of a spectrogram.
Parameters
----------... | python | {
"resource": ""
} |
q267971 | zero_crossing_rate | test | def zero_crossing_rate(y, frame_length=2048, hop_length=512, center=True,
**kwargs):
'''Compute the zero-crossing rate of an audio time series.
Parameters
----------
y : np.ndarray [shape=(n,)]
Audio time series
frame_length : int > 0
Length of the frame over... | python | {
"resource": ""
} |
q267972 | chroma_stft | test | def chroma_stft(y=None, sr=22050, S=None, norm=np.inf, n_fft=2048,
hop_length=512, win_length=None, window='hann', center=True,
pad_mode='reflect', tuning=None, **kwargs):
"""Compute a chromagram from a waveform or power spectrogram.
This implementation is derived from `chromagr... | python | {
"resource": ""
} |
q267973 | chroma_cqt | test | def chroma_cqt(y=None, sr=22050, C=None, hop_length=512, fmin=None,
norm=np.inf, threshold=0.0, tuning=None, n_chroma=12,
n_octaves=7, window=None, bins_per_octave=None, cqt_mode='full'):
r'''Constant-Q chromagram
Parameters
----------
y : np.ndarray [shape=(n,)]
a... | python | {
"resource": ""
} |
q267974 | melspectrogram | test | def melspectrogram(y=None, sr=22050, S=None, n_fft=2048, hop_length=512,
win_length=None, window='hann', center=True, pad_mode='reflect',
power=2.0, **kwargs):
"""Compute a mel-scaled spectrogram.
If a spectrogram input `S` is provided, then it is mapped directly onto
... | python | {
"resource": ""
} |
q267975 | __jaccard | test | def __jaccard(int_a, int_b): # pragma: no cover
'''Jaccard similarity between two intervals
Parameters
----------
int_a, int_b : np.ndarrays, shape=(2,)
Returns
-------
Jaccard similarity between intervals
'''
ends = [int_a[1], int_b[1]]
if ends[1] < ends[0]:
ends.reve... | python | {
"resource": ""
} |
q267976 | __match_interval_overlaps | test | def __match_interval_overlaps(query, intervals_to, candidates): # pragma: no cover
'''Find the best Jaccard match from query to candidates'''
best_score = -1
best_idx = -1
for idx in candidates:
score = __jaccard(query, intervals_to[idx])
if score > best_score:
best_score,... | python | {
"resource": ""
} |
q267977 | __match_intervals | test | def __match_intervals(intervals_from, intervals_to, strict=True): # pragma: no cover
'''Numba-accelerated interval matching algorithm.
'''
# sort index of the interval starts
start_index = np.argsort(intervals_to[:, 0])
# sort index of the interval ends
end_index = np.argsort(intervals_to[:, ... | python | {
"resource": ""
} |
q267978 | match_intervals | test | def match_intervals(intervals_from, intervals_to, strict=True):
'''Match one set of time intervals to another.
This can be useful for tasks such as mapping beat timings
to segments.
Each element `[a, b]` of `intervals_from` is matched to the
element `[c, d]` of `intervals_to` which maximizes the
... | python | {
"resource": ""
} |
q267979 | match_events | test | def match_events(events_from, events_to, left=True, right=True):
'''Match one set of events to another.
This is useful for tasks such as matching beats to the nearest
detected onsets, or frame-aligned events to the nearest zero-crossing.
.. note:: A target event may be matched to multiple source event... | python | {
"resource": ""
} |
q267980 | salience | test | def salience(S, freqs, h_range, weights=None, aggregate=None,
filter_peaks=True, fill_value=np.nan, kind='linear', axis=0):
"""Harmonic salience function.
Parameters
----------
S : np.ndarray [shape=(d, n)]
input time frequency magnitude representation (stft, ifgram, etc).
... | python | {
"resource": ""
} |
q267981 | interp_harmonics | test | def interp_harmonics(x, freqs, h_range, kind='linear', fill_value=0, axis=0):
'''Compute the energy at harmonics of time-frequency representation.
Given a frequency-based energy representation such as a spectrogram
or tempogram, this function computes the energy at the chosen harmonics
of the frequency... | python | {
"resource": ""
} |
q267982 | harmonics_1d | test | def harmonics_1d(harmonic_out, x, freqs, h_range, kind='linear',
fill_value=0, axis=0):
'''Populate a harmonic tensor from a time-frequency representation.
Parameters
----------
harmonic_out : np.ndarray, shape=(len(h_range), X.shape)
The output array to store harmonics
X ... | python | {
"resource": ""
} |
q267983 | harmonics_2d | test | def harmonics_2d(harmonic_out, x, freqs, h_range, kind='linear', fill_value=0,
axis=0):
'''Populate a harmonic tensor from a time-frequency representation with
time-varying frequencies.
Parameters
----------
harmonic_out : np.ndarray
The output array to store harmonics
... | python | {
"resource": ""
} |
q267984 | load | test | def load(path, sr=22050, mono=True, offset=0.0, duration=None,
dtype=np.float32, res_type='kaiser_best'):
"""Load an audio file as a floating point time series.
Audio will be automatically resampled to the given rate
(default `sr=22050`).
To preserve the native sampling rate of the file, use ... | python | {
"resource": ""
} |
q267985 | __audioread_load | test | def __audioread_load(path, offset, duration, dtype):
'''Load an audio buffer using audioread.
This loads one block at a time, and then concatenates the results.
'''
y = []
with audioread.audio_open(path) as input_file:
sr_native = input_file.samplerate
n_channels = input_file.chann... | python | {
"resource": ""
} |
q267986 | to_mono | test | def to_mono(y):
'''Force an audio signal down to mono.
Parameters
----------
y : np.ndarray [shape=(2,n) or shape=(n,)]
audio time series, either stereo or mono
Returns
-------
y_mono : np.ndarray [shape=(n,)]
`y` as a monophonic time-series
Notes
-----
This fu... | python | {
"resource": ""
} |
q267987 | resample | test | def resample(y, orig_sr, target_sr, res_type='kaiser_best', fix=True, scale=False, **kwargs):
"""Resample a time series from orig_sr to target_sr
Parameters
----------
y : np.ndarray [shape=(n,) or shape=(2, n)]
audio time series. Can be mono or stereo.
orig_sr : number > 0 [scalar]
... | python | {
"resource": ""
} |
q267988 | autocorrelate | test | def autocorrelate(y, max_size=None, axis=-1):
"""Bounded auto-correlation
Parameters
----------
y : np.ndarray
array to autocorrelate
max_size : int > 0 or None
maximum correlation lag.
If unspecified, defaults to `y.shape[axis]` (unbounded)
axis : int
The axi... | python | {
"resource": ""
} |
q267989 | lpc | test | def lpc(y, order):
"""Linear Prediction Coefficients via Burg's method
This function applies Burg's method to estimate coefficients of a linear
filter on `y` of order `order`. Burg's method is an extension to the
Yule-Walker approach, which are both sometimes referred to as LPC parameter
estimatio... | python | {
"resource": ""
} |
q267990 | clicks | test | def clicks(times=None, frames=None, sr=22050, hop_length=512,
click_freq=1000.0, click_duration=0.1, click=None, length=None):
"""Returns a signal with the signal `click` placed at each specified time
Parameters
----------
times : np.ndarray or None
times to place clicks, in seconds
... | python | {
"resource": ""
} |
q267991 | tone | test | def tone(frequency, sr=22050, length=None, duration=None, phi=None):
"""Returns a pure tone signal. The signal generated is a cosine wave.
Parameters
----------
frequency : float > 0
frequency
sr : number > 0
desired sampling rate of the output signal
length : int > 0
... | python | {
"resource": ""
} |
q267992 | chirp | test | def chirp(fmin, fmax, sr=22050, length=None, duration=None, linear=False, phi=None):
"""Returns a chirp signal that goes from frequency `fmin` to frequency `fmax`
Parameters
----------
fmin : float > 0
initial frequency
fmax : float > 0
final frequency
sr : number > 0
... | python | {
"resource": ""
} |
q267993 | __get_files | test | def __get_files(dir_name, extensions):
'''Helper function to get files in a single directory'''
# Expand out the directory
dir_name = os.path.abspath(os.path.expanduser(dir_name))
myfiles = set()
for sub_ext in extensions:
globstr = os.path.join(dir_name, '*' + os.path.extsep + sub_ext)
... | python | {
"resource": ""
} |
q267994 | stretch_demo | test | def stretch_demo(input_file, output_file, speed):
'''Phase-vocoder time stretch demo function.
:parameters:
- input_file : str
path to input audio
- output_file : str
path to save output (wav)
- speed : float > 0
speed up by this factor
'''
# 1. Load the... | python | {
"resource": ""
} |
q267995 | process_arguments | test | def process_arguments(args):
'''Argparse function to get the program parameters'''
parser = argparse.ArgumentParser(description='Time stretching example')
parser.add_argument('input_file',
action='store',
help='path to the input file (wav, mp3, etc)')
p... | python | {
"resource": ""
} |
q267996 | hpss_demo | test | def hpss_demo(input_file, output_harmonic, output_percussive):
'''HPSS demo function.
:parameters:
- input_file : str
path to input audio
- output_harmonic : str
path to save output harmonic (wav)
- output_percussive : str
path to save output harmonic (wav)
'... | python | {
"resource": ""
} |
q267997 | beat_track | test | def beat_track(y=None, sr=22050, onset_envelope=None, hop_length=512,
start_bpm=120.0, tightness=100, trim=True, bpm=None,
units='frames'):
r'''Dynamic programming beat tracker.
Beats are detected in three stages, following the method of [1]_:
1. Measure onset strength
... | python | {
"resource": ""
} |
q267998 | __beat_tracker | test | def __beat_tracker(onset_envelope, bpm, fft_res, tightness, trim):
"""Internal function that tracks beats in an onset strength envelope.
Parameters
----------
onset_envelope : np.ndarray [shape=(n,)]
onset strength envelope
bpm : float [scalar]
tempo estimate
fft_res : float ... | python | {
"resource": ""
} |
q267999 | __beat_local_score | test | def __beat_local_score(onset_envelope, period):
'''Construct the local score for an onset envlope and given period'''
window = np.exp(-0.5 * (np.arange(-period, period+1)*32.0/period)**2)
return scipy.signal.convolve(__normalize_onsets(onset_envelope),
window,
... | python | {
"resource": ""
} |
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