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Source code for librosa.onset

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Onset detection
===============
.. autosummary::
    :toctree: generated/

    onset_detect
    onset_backtrack
    onset_strength
    onset_strength_multi
"""

import numpy as np
import scipy

from ._cache import cache
from . import core
from . import util
from .util.exceptions import ParameterError

from .feature.spectral import melspectrogram

__all__ = ['onset_detect',
           'onset_strength',
           'onset_strength_multi',
           'onset_backtrack']


[docs]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 large-scale hyper-parameter optimization over the dataset provided by [1]_. .. [1] https://github.com/CPJKU/onset_db Parameters ---------- y : np.ndarray [shape=(n,)] audio time series sr : number > 0 [scalar] sampling rate of `y` onset_envelope : np.ndarray [shape=(m,)] (optional) pre-computed onset strength envelope hop_length : int > 0 [scalar] hop length (in samples) units : {'frames', 'samples', 'time'} The units to encode detected onset events in. By default, 'frames' are used. backtrack : bool If `True`, detected onset events are backtracked to the nearest preceding minimum of `energy`. This is primarily useful when using onsets as slice points for segmentation. energy : np.ndarray [shape=(m,)] (optional) An energy function to use for backtracking detected onset events. If none is provided, then `onset_envelope` is used. kwargs : additional keyword arguments Additional parameters for peak picking. See `librosa.util.peak_pick` for details. Returns ------- onsets : np.ndarray [shape=(n_onsets,)] estimated positions of detected onsets, in whichever units are specified. By default, frame indices. .. note:: If no onset strength could be detected, onset_detect returns an empty list. Raises ------ ParameterError if neither `y` nor `onsets` are provided or if `units` is not one of 'frames', 'samples', or 'time' See Also -------- onset_strength : compute onset strength per-frame onset_backtrack : backtracking onset events librosa.util.peak_pick : pick peaks from a time series Examples -------- Get onset times from a signal >>> y, sr = librosa.load(librosa.util.example_audio_file(), ... offset=30, duration=2.0) >>> onset_frames = librosa.onset.onset_detect(y=y, sr=sr) >>> librosa.frames_to_time(onset_frames, sr=sr) array([ 0.07 , 0.395, 0.511, 0.627, 0.766, 0.975, 1.207, 1.324, 1.44 , 1.788, 1.881]) Or use a pre-computed onset envelope >>> o_env = librosa.onset.onset_strength(y, sr=sr) >>> times = librosa.times_like(o_env, sr=sr) >>> onset_frames = librosa.onset.onset_detect(onset_envelope=o_env, sr=sr) >>> import matplotlib.pyplot as plt >>> D = np.abs(librosa.stft(y)) >>> plt.figure() >>> ax1 = plt.subplot(2, 1, 1) >>> librosa.display.specshow(librosa.amplitude_to_db(D, ref=np.max), ... x_axis='time', y_axis='log') >>> plt.title('Power spectrogram') >>> plt.subplot(2, 1, 2, sharex=ax1) >>> plt.plot(times, o_env, label='Onset strength') >>> plt.vlines(times[onset_frames], 0, o_env.max(), color='r', alpha=0.9, ... linestyle='--', label='Onsets') >>> plt.axis('tight') >>> plt.legend(frameon=True, framealpha=0.75) >>> plt.show() """ # First, get the frame->beat strength profile if we don't already have one if onset_envelope is None: if y is None: raise ParameterError('y or onset_envelope must be provided') onset_envelope = onset_strength(y=y, sr=sr, hop_length=hop_length) # Shift onset envelope up to be non-negative # (a common normalization step to make the threshold more consistent) onset_envelope -= onset_envelope.min() # Do we have any onsets to grab? if not onset_envelope.any(): return np.array([], dtype=np.int) # Normalize onset strength function to [0, 1] range onset_envelope /= onset_envelope.max() # These parameter settings found by large-scale search kwargs.setdefault('pre_max', 0.03*sr//hop_length) # 30ms kwargs.setdefault('post_max', 0.00*sr//hop_length + 1) # 0ms kwargs.setdefault('pre_avg', 0.10*sr//hop_length) # 100ms kwargs.setdefault('post_avg', 0.10*sr//hop_length + 1) # 100ms kwargs.setdefault('wait', 0.03*sr//hop_length) # 30ms kwargs.setdefault('delta', 0.07) # Peak pick the onset envelope onsets = util.peak_pick(onset_envelope, **kwargs) # Optionally backtrack the events if backtrack: if energy is None: energy = onset_envelope onsets = onset_backtrack(onsets, energy) if units == 'frames': pass elif units == 'samples': onsets = core.frames_to_samples(onsets, hop_length=hop_length) elif units == 'time': onsets = core.frames_to_time(onsets, hop_length=hop_length, sr=sr) else: raise ParameterError('Invalid unit type: {}'.format(units)) return onsets
[docs]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 strength at time `t` is determined by: `mean_f max(0, S[f, t] - ref[f, t - lag])` where `ref` is `S` after local max filtering along the frequency axis [1]_. By default, if a time series `y` is provided, S will be the log-power Mel spectrogram. .. [1] Böck, Sebastian, and Gerhard Widmer. "Maximum filter vibrato suppression for onset detection." 16th International Conference on Digital Audio Effects, Maynooth, Ireland. 2013. Parameters ---------- y : np.ndarray [shape=(n,)] audio time-series sr : number > 0 [scalar] sampling rate of `y` S : np.ndarray [shape=(d, m)] pre-computed (log-power) spectrogram lag : int > 0 time lag for computing differences max_size : int > 0 size (in frequency bins) of the local max filter. set to `1` to disable filtering. ref : None or np.ndarray [shape=(d, m)] An optional pre-computed reference spectrum, of the same shape as `S`. If not provided, it will be computed from `S`. If provided, it will override any local max filtering governed by `max_size`. detrend : bool [scalar] Filter the onset strength to remove the DC component center : bool [scalar] Shift the onset function by `n_fft / (2 * hop_length)` frames feature : function Function for computing time-series features, eg, scaled spectrograms. By default, uses `librosa.feature.melspectrogram` with `fmax=11025.0` aggregate : function Aggregation function to use when combining onsets at different frequency bins. Default: `np.mean` kwargs : additional keyword arguments Additional parameters to `feature()`, if `S` is not provided. Returns ------- onset_envelope : np.ndarray [shape=(m,)] vector containing the onset strength envelope Raises ------ ParameterError if neither `(y, sr)` nor `S` are provided or if `lag` or `max_size` are not positive integers See Also -------- onset_detect onset_strength_multi Examples -------- First, load some audio and plot the spectrogram >>> import matplotlib.pyplot as plt >>> y, sr = librosa.load(librosa.util.example_audio_file(), ... duration=10.0) >>> D = np.abs(librosa.stft(y)) >>> times = librosa.times_like(D) >>> plt.figure() >>> ax1 = plt.subplot(2, 1, 1) >>> librosa.display.specshow(librosa.amplitude_to_db(D, ref=np.max), ... y_axis='log', x_axis='time') >>> plt.title('Power spectrogram') Construct a standard onset function >>> onset_env = librosa.onset.onset_strength(y=y, sr=sr) >>> plt.subplot(2, 1, 2, sharex=ax1) >>> plt.plot(times, 2 + onset_env / onset_env.max(), alpha=0.8, ... label='Mean (mel)') Median aggregation, and custom mel options >>> onset_env = librosa.onset.onset_strength(y=y, sr=sr, ... aggregate=np.median, ... fmax=8000, n_mels=256) >>> plt.plot(times, 1 + onset_env / onset_env.max(), alpha=0.8, ... label='Median (custom mel)') Constant-Q spectrogram instead of Mel >>> C = np.abs(librosa.cqt(y=y, sr=sr)) >>> onset_env = librosa.onset.onset_strength(sr=sr, S=librosa.amplitude_to_db(C, ref=np.max)) >>> plt.plot(times, onset_env / onset_env.max(), alpha=0.8, ... label='Mean (CQT)') >>> plt.legend(frameon=True, framealpha=0.75) >>> plt.ylabel('Normalized strength') >>> plt.yticks([]) >>> plt.axis('tight') >>> plt.tight_layout() >>> plt.show() """ if aggregate is False: raise ParameterError('aggregate={} cannot be False when computing full-spectrum onset strength.') odf_all = onset_strength_multi(y=y, sr=sr, S=S, lag=lag, max_size=max_size, ref=ref, detrend=detrend, center=center, feature=feature, aggregate=aggregate, channels=None, **kwargs) return odf_all[0]
[docs]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 to determine slice points for segmentation, as described by [1]_. .. [1] Jehan, Tristan. "Creating music by listening" Doctoral dissertation Massachusetts Institute of Technology, 2005. Parameters ---------- events : np.ndarray, dtype=int List of onset event frame indices, as computed by `onset_detect` energy : np.ndarray, shape=(m,) An energy function Returns ------- events_backtracked : np.ndarray, shape=events.shape The input events matched to nearest preceding minima of `energy`. Examples -------- >>> y, sr = librosa.load(librosa.util.example_audio_file(), ... offset=30, duration=2.0) >>> oenv = librosa.onset.onset_strength(y=y, sr=sr) >>> # Detect events without backtracking >>> onset_raw = librosa.onset.onset_detect(onset_envelope=oenv, ... backtrack=False) >>> # Backtrack the events using the onset envelope >>> onset_bt = librosa.onset.onset_backtrack(onset_raw, oenv) >>> # Backtrack the events using the RMS values >>> rms = librosa.feature.rms(S=np.abs(librosa.stft(y=y))) >>> onset_bt_rms = librosa.onset.onset_backtrack(onset_raw, rms[0]) >>> # Plot the results >>> import matplotlib.pyplot as plt >>> plt.figure() >>> plt.subplot(2,1,1) >>> plt.plot(oenv, label='Onset strength') >>> plt.vlines(onset_raw, 0, oenv.max(), label='Raw onsets') >>> plt.vlines(onset_bt, 0, oenv.max(), label='Backtracked', color='r') >>> plt.legend(frameon=True, framealpha=0.75) >>> plt.subplot(2,1,2) >>> plt.plot(rms[0], label='RMS') >>> plt.vlines(onset_bt_rms, 0, rms.max(), label='Backtracked (RMS)', color='r') >>> plt.legend(frameon=True, framealpha=0.75) >>> plt.show() ''' # Find points where energy is non-increasing # all points: energy[i] <= energy[i-1] # tail points: energy[i] < energy[i+1] minima = np.flatnonzero((energy[1:-1] <= energy[:-2]) & (energy[1:-1] < energy[2:])) # Pad on a 0, just in case we have onsets with no preceding minimum # Shift by one to account for slicing in minima detection minima = util.fix_frames(1 + minima, x_min=0) # Only match going left from the detected events return minima[util.match_events(events, minima, right=False)]
[docs]@cache(level=30) def onset_strength_multi(y=None, sr=22050, S=None, n_fft=2048, hop_length=512, 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 channel `i` at time `t` is determined by: `mean_{f in channels[i]} max(0, S[f, t+1] - S[f, t])` Parameters ---------- y : np.ndarray [shape=(n,)] audio time-series sr : number > 0 [scalar] sampling rate of `y` S : np.ndarray [shape=(d, m)] pre-computed (log-power) spectrogram n_fft : int > 0 [scalar] FFT window size for use in `feature()` if `S` is not provided. hop_length : int > 0 [scalar] hop length for use in `feature()` if `S` is not provided. lag : int > 0 time lag for computing differences max_size : int > 0 size (in frequency bins) of the local max filter. set to `1` to disable filtering. ref : None or np.ndarray [shape=(d, m)] An optional pre-computed reference spectrum, of the same shape as `S`. If not provided, it will be computed from `S`. If provided, it will override any local max filtering governed by `max_size`. detrend : bool [scalar] Filter the onset strength to remove the DC component center : bool [scalar] Shift the onset function by `n_fft / (2 * hop_length)` frames feature : function Function for computing time-series features, eg, scaled spectrograms. By default, uses `librosa.feature.melspectrogram` with `fmax=11025.0` Must support arguments: `y, sr, n_fft, hop_length` aggregate : function or False Aggregation function to use when combining onsets at different frequency bins. If `False`, then no aggregation is performed. Default: `np.mean` channels : list or None Array of channel boundaries or slice objects. If `None`, then a single channel is generated to span all bands. kwargs : additional keyword arguments Additional parameters to `feature()`, if `S` is not provided. Returns ------- onset_envelope : np.ndarray [shape=(n_channels, m)] array containing the onset strength envelope for each specified channel Raises ------ ParameterError if neither `(y, sr)` nor `S` are provided See Also -------- onset_strength Notes ----- This function caches at level 30. Examples -------- First, load some audio and plot the spectrogram >>> import matplotlib.pyplot as plt >>> y, sr = librosa.load(librosa.util.example_audio_file(), ... duration=10.0) >>> D = np.abs(librosa.stft(y)) >>> plt.figure() >>> plt.subplot(2, 1, 1) >>> librosa.display.specshow(librosa.amplitude_to_db(D, ref=np.max), ... y_axis='log') >>> plt.title('Power spectrogram') Construct a standard onset function over four sub-bands >>> onset_subbands = librosa.onset.onset_strength_multi(y=y, sr=sr, ... channels=[0, 32, 64, 96, 128]) >>> plt.subplot(2, 1, 2) >>> librosa.display.specshow(onset_subbands, x_axis='time') >>> plt.ylabel('Sub-bands') >>> plt.title('Sub-band onset strength') >>> plt.show() """ if feature is None: feature = melspectrogram kwargs.setdefault('fmax', 11025.0) if aggregate is None: aggregate = np.mean if lag < 1 or not isinstance(lag, int): raise ParameterError('lag must be a positive integer') if max_size < 1 or not isinstance(max_size, int): raise ParameterError('max_size must be a positive integer') # First, compute mel spectrogram if S is None: S = np.abs(feature(y=y, sr=sr, n_fft=n_fft, hop_length=hop_length, **kwargs)) # Convert to dBs S = core.power_to_db(S) # Ensure that S is at least 2-d S = np.atleast_2d(S) # Compute the reference spectrogram. # Efficiency hack: skip filtering step and pass by reference # if max_size will produce a no-op. if ref is None: if max_size == 1: ref = S else: ref = scipy.ndimage.maximum_filter1d(S, max_size, axis=0) elif ref.shape != S.shape: raise ParameterError('Reference spectrum shape {} must match input spectrum {}'.format(ref.shape, S.shape)) # Compute difference to the reference, spaced by lag onset_env = S[:, lag:] - ref[:, :-lag] # Discard negatives (decreasing amplitude) onset_env = np.maximum(0.0, onset_env) # Aggregate within channels pad = True if channels is None: channels = [slice(None)] else: pad = False if aggregate: onset_env = util.sync(onset_env, channels, aggregate=aggregate, pad=pad, axis=0) # compensate for lag pad_width = lag if center: # Counter-act framing effects. Shift the onsets by n_fft / hop_length pad_width += n_fft // (2 * hop_length) onset_env = np.pad(onset_env, ([0, 0], [int(pad_width), 0]), mode='constant') # remove the DC component if detrend: onset_env = scipy.signal.lfilter([1.0, -1.0], [1.0, -0.99], onset_env, axis=-1) # Trim to match the input duration if center: onset_env = onset_env[:, :S.shape[1]] return onset_env