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Source code for librosa.feature.rhythm
#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''Rhythmic feature extraction'''
import numpy as np
from .. import util
from ..core.audio import autocorrelate
from ..core.spectrum import stft
from ..util.exceptions import ParameterError
from ..filters import get_window
__all__ = ['tempogram', 'fourier_tempogram']
# -- Rhythmic features -- #
[docs]def tempogram(y=None, sr=22050, onset_envelope=None, hop_length=512,
win_length=384, center=True, window='hann', norm=np.inf):
'''Compute the tempogram: local autocorrelation of the onset strength envelope. [1]_
.. [1] Grosche, Peter, Meinard Müller, and Frank Kurth.
"Cyclic tempogram - A mid-level tempo representation for music signals."
ICASSP, 2010.
Parameters
----------
y : np.ndarray [shape=(n,)] or None
Audio time series.
sr : number > 0 [scalar]
sampling rate of `y`
onset_envelope : np.ndarray [shape=(n,) or (m, n)] or None
Optional pre-computed onset strength envelope as provided by
`onset.onset_strength`.
If multi-dimensional, tempograms are computed independently for each
band (first dimension).
hop_length : int > 0
number of audio samples between successive onset measurements
win_length : int > 0
length of the onset autocorrelation window (in frames/onset measurements)
The default settings (384) corresponds to `384 * hop_length / sr ~= 8.9s`.
center : bool
If `True`, onset autocorrelation windows are centered.
If `False`, windows are left-aligned.
window : string, function, number, tuple, or np.ndarray [shape=(win_length,)]
A window specification as in `core.stft`.
norm : {np.inf, -np.inf, 0, float > 0, None}
Normalization mode. Set to `None` to disable normalization.
Returns
-------
tempogram : np.ndarray [shape=(win_length, n) or (m, win_length, n)]
Localized autocorrelation of the onset strength envelope.
If given multi-band input (`onset_envelope.shape==(m,n)`) then
`tempogram[i]` is the tempogram of `onset_envelope[i]`.
Raises
------
ParameterError
if neither `y` nor `onset_envelope` are provided
if `win_length < 1`
See Also
--------
fourier_tempogram
librosa.onset.onset_strength
librosa.util.normalize
librosa.core.stft
Examples
--------
>>> # Compute local onset autocorrelation
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> hop_length = 512
>>> oenv = librosa.onset.onset_strength(y=y, sr=sr, hop_length=hop_length)
>>> tempogram = librosa.feature.tempogram(onset_envelope=oenv, sr=sr,
... hop_length=hop_length)
>>> # Compute global onset autocorrelation
>>> ac_global = librosa.autocorrelate(oenv, max_size=tempogram.shape[0])
>>> ac_global = librosa.util.normalize(ac_global)
>>> # Estimate the global tempo for display purposes
>>> tempo = librosa.beat.tempo(onset_envelope=oenv, sr=sr,
... hop_length=hop_length)[0]
>>> import matplotlib.pyplot as plt
>>> plt.figure(figsize=(8, 8))
>>> plt.subplot(4, 1, 1)
>>> plt.plot(oenv, label='Onset strength')
>>> plt.xticks([])
>>> plt.legend(frameon=True)
>>> plt.axis('tight')
>>> plt.subplot(4, 1, 2)
>>> # We'll truncate the display to a narrower range of tempi
>>> librosa.display.specshow(tempogram, sr=sr, hop_length=hop_length,
>>> x_axis='time', y_axis='tempo')
>>> plt.axhline(tempo, color='w', linestyle='--', alpha=1,
... label='Estimated tempo={:g}'.format(tempo))
>>> plt.legend(frameon=True, framealpha=0.75)
>>> plt.subplot(4, 1, 3)
>>> x = np.linspace(0, tempogram.shape[0] * float(hop_length) / sr,
... num=tempogram.shape[0])
>>> plt.plot(x, np.mean(tempogram, axis=1), label='Mean local autocorrelation')
>>> plt.plot(x, ac_global, '--', alpha=0.75, label='Global autocorrelation')
>>> plt.xlabel('Lag (seconds)')
>>> plt.axis('tight')
>>> plt.legend(frameon=True)
>>> plt.subplot(4,1,4)
>>> # We can also plot on a BPM axis
>>> freqs = librosa.tempo_frequencies(tempogram.shape[0], hop_length=hop_length, sr=sr)
>>> plt.semilogx(freqs[1:], np.mean(tempogram[1:], axis=1),
... label='Mean local autocorrelation', basex=2)
>>> plt.semilogx(freqs[1:], ac_global[1:], '--', alpha=0.75,
... label='Global autocorrelation', basex=2)
>>> plt.axvline(tempo, color='black', linestyle='--', alpha=.8,
... label='Estimated tempo={:g}'.format(tempo))
>>> plt.legend(frameon=True)
>>> plt.xlabel('BPM')
>>> plt.axis('tight')
>>> plt.grid()
>>> plt.tight_layout()
>>> plt.show()
'''
from ..onset import onset_strength
if win_length < 1:
raise ParameterError('win_length must be a positive integer')
ac_window = get_window(window, win_length, fftbins=True)
if onset_envelope is None:
if y is None:
raise ParameterError('Either y or onset_envelope must be provided')
onset_envelope = onset_strength(y=y, sr=sr, hop_length=hop_length)
else:
# Force row-contiguity to avoid framing errors below
onset_envelope = np.ascontiguousarray(onset_envelope)
if onset_envelope.ndim > 1:
# If we have multi-band input, iterate over rows
return np.asarray([tempogram(onset_envelope=oe_subband,
hop_length=hop_length,
win_length=win_length,
center=center,
window=window,
norm=norm) for oe_subband in onset_envelope])
# Center the autocorrelation windows
n = len(onset_envelope)
if center:
onset_envelope = np.pad(onset_envelope, int(win_length // 2),
mode='linear_ramp', end_values=[0, 0])
# Carve onset envelope into frames
odf_frame = util.frame(onset_envelope,
frame_length=win_length,
hop_length=1)
# Truncate to the length of the original signal
if center:
odf_frame = odf_frame[:, :n]
# Window, autocorrelate, and normalize
return util.normalize(autocorrelate(odf_frame * ac_window[:, np.newaxis],
axis=0),
norm=norm, axis=0)
[docs]def fourier_tempogram(y=None, sr=22050, onset_envelope=None, hop_length=512,
win_length=384, center=True, window='hann'):
'''Compute the Fourier tempogram: the short-time Fourier transform of the
onset strength envelope. [1]_
.. [1] Grosche, Peter, Meinard Müller, and Frank Kurth.
"Cyclic tempogram - A mid-level tempo representation for music signals."
ICASSP, 2010.
Parameters
----------
y : np.ndarray [shape=(n,)] or None
Audio time series.
sr : number > 0 [scalar]
sampling rate of `y`
onset_envelope : np.ndarray [shape=(n,)] or None
Optional pre-computed onset strength envelope as provided by
`onset.onset_strength`.
hop_length : int > 0
number of audio samples between successive onset measurements
win_length : int > 0
length of the onset window (in frames/onset measurements)
The default settings (384) corresponds to `384 * hop_length / sr ~= 8.9s`.
center : bool
If `True`, onset windows are centered.
If `False`, windows are left-aligned.
window : string, function, number, tuple, or np.ndarray [shape=(win_length,)]
A window specification as in `core.stft`.
Returns
-------
tempogram : np.ndarray [shape=(win_length // 2 + 1, n)]
Complex short-time Fourier transform of the onset envelope.
Raises
------
ParameterError
if neither `y` nor `onset_envelope` are provided
if `win_length < 1`
See Also
--------
tempogram
librosa.onset.onset_strength
librosa.util.normalize
librosa.core.stft
Examples
--------
>>> # Compute local onset autocorrelation
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> hop_length = 512
>>> oenv = librosa.onset.onset_strength(y=y, sr=sr, hop_length=hop_length)
>>> tempogram = librosa.feature.fourier_tempogram(onset_envelope=oenv, sr=sr,
... hop_length=hop_length)
>>> # Compute the auto-correlation tempogram, unnormalized to make comparison easier
>>> ac_tempogram = librosa.feature.tempogram(onset_envelope=oenv, sr=sr,
... hop_length=hop_length, norm=None)
>>> import matplotlib.pyplot as plt
>>> plt.figure(figsize=(8, 8))
>>> plt.subplot(3, 1, 1)
>>> plt.plot(oenv, label='Onset strength')
>>> plt.xticks([])
>>> plt.legend(frameon=True)
>>> plt.axis('tight')
>>> plt.subplot(3, 1, 2)
>>> librosa.display.specshow(np.abs(tempogram), sr=sr, hop_length=hop_length,
>>> x_axis='time', y_axis='fourier_tempo', cmap='magma')
>>> plt.title('Fourier tempogram')
>>> plt.subplot(3, 1, 3)
>>> librosa.display.specshow(ac_tempogram, sr=sr, hop_length=hop_length,
>>> x_axis='time', y_axis='tempo', cmap='magma')
>>> plt.title('Autocorrelation tempogram')
>>> plt.tight_layout()
>>> plt.show()
'''
from ..onset import onset_strength
if win_length < 1:
raise ParameterError('win_length must be a positive integer')
if onset_envelope is None:
if y is None:
raise ParameterError('Either y or onset_envelope must be provided')
onset_envelope = onset_strength(y=y, sr=sr, hop_length=hop_length)
else:
# Force row-contiguity to avoid framing errors below
onset_envelope = np.ascontiguousarray(onset_envelope)
# Generate the short-time Fourier transform
return stft(onset_envelope, n_fft=win_length, hop_length=1,
center=center, window=window)