Source code for librosa.feature.rhythm


# -*- coding: utf-8 -*-
'''Rhythmic feature extraction'''

import numpy as np
import scipy.signal
import six

from .. import util

from ..core.audio import autocorrelate
from ..util.exceptions import ParameterError


__all__ = ['tempogram']


# -- Rhythmic features -- #
[docs]def tempogram(y=None, sr=22050, onset_envelope=None, hop_length=512, win_length=384, center=True, window=None, 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 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 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 : None, function, np.ndarray [shape=(win_length,)] Window function to apply to onset strength function. By default (`None`), an asymmetric Hann window. 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)] Localized autocorrelation of the onset strength envelope Raises ------ ParameterError if neither `y` nor `onset_envelope` are provided if `win_length < 1` if `window` is an array and `len(window) != win_length` See Also -------- 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.estimate_tempo(oenv, sr=sr, hop_length=hop_length) >>> import matplotlib.pyplot as plt >>> plt.figure(figsize=(8, 6)) >>> 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) >>> # We'll truncate the display to a narrower range of tempi >>> librosa.display.specshow(tempogram[:100], sr=sr, hop_length=hop_length, >>> x_axis='time', y_axis='tempo', ... tmin=tempo/4, tmax=2*tempo, n_yticks=4) >>> plt.subplot(3, 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.tight_layout() ''' from ..onset import onset_strength if win_length < 1: raise ParameterError('win_length must be a positive integer') if window is None: ac_window = scipy.signal.hann(win_length, sym=False) elif six.callable(window): ac_window = window(win_length) else: ac_window = np.asarray(window) if ac_window.size != win_length: raise ParameterError('Size mismatch between win_length and len(window)') 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) # Pad the envelope so that autocorrelation windows are centered on the input 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)