librosa.beat.beat_track

librosa.beat.beat_track(*, y=None, sr=22050, onset_envelope=None, hop_length=512, start_bpm=120.0, tightness=100, trim=True, bpm=None, prior=None, units='frames', sparse=True)[source]

Dynamic programming beat tracker.

Beats are detected in three stages, following the method of [1]:

  1. Measure onset strength

  2. Estimate tempo from onset correlation

  3. Pick peaks in onset strength approximately consistent with estimated tempo

Parameters:
ynp.ndarray [shape=(…, n)] or None

audio time series

srnumber > 0 [scalar]

sampling rate of y

onset_envelopenp.ndarray [shape=(…, m)] or None

(optional) pre-computed onset strength envelope.

hop_lengthint > 0 [scalar]

number of audio samples between successive onset_envelope values

start_bpmfloat > 0 [scalar]

initial guess for the tempo estimator (in beats per minute)

tightnessfloat [scalar]

tightness of beat distribution around tempo

trimbool [scalar]

trim leading/trailing beats with weak onsets

bpmfloat [scalar] or np.ndarray [shape=(…)]

(optional) If provided, use bpm as the tempo instead of estimating it from onsets.

If multichannel, tempo estimates can be provided for all channels.

Tempo estimates may also be time-varying, in which case the shape of bpm should match that of onset_envelope, i.e., one estimate provided for each frame.

priorscipy.stats.rv_continuous [optional]

An optional prior distribution over tempo. If provided, start_bpm will be ignored.

units{‘frames’, ‘samples’, ‘time’}

The units to encode detected beat events in. By default, ‘frames’ are used.

sparsebool

If True (default), detections are returned as an array of frames, samples, or time indices (as specified by units=).

If False, detections are encoded as a dense boolean array where beats[..., n] is true if there’s a beat at frame index n.

Note

multi-channel input is only supported when sparse=False.

Returns:
tempofloat [scalar, non-negative] or np.ndarray

estimated global tempo (in beats per minute)

If multi-channel and bpm is not provided, a separate tempo will be returned for each channel

beatsnp.ndarray

estimated beat event locations.

If sparse=True (default), beat locations are given in the specified units (default is frame indices).

If sparse=False (required for multichannel input), beat events are indicated by a boolean for each frame.

Note

If no onset strength could be detected, beat_tracker estimates 0 BPM and returns an empty list.

Raises:
ParameterError

if neither y nor onset_envelope are provided, or if units is not one of ‘frames’, ‘samples’, or ‘time’

See also

librosa.onset.onset_strength

Examples

Track beats using time series input

>>> y, sr = librosa.load(librosa.ex('choice'), duration=10)

>>> tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
>>> tempo
135.99917763157896

Print the frames corresponding to beats

>>> beats
array([  3,  21,  40,  59,  78,  96, 116, 135, 154, 173, 192, 211,
       230, 249, 268, 287, 306, 325, 344, 363])

Or print them as timestamps

>>> librosa.frames_to_time(beats, sr=sr)
array([0.07 , 0.488, 0.929, 1.37 , 1.811, 2.229, 2.694, 3.135,
       3.576, 4.017, 4.458, 4.899, 5.341, 5.782, 6.223, 6.664,
       7.105, 7.546, 7.988, 8.429])

Output beat detections as a boolean array instead of frame indices >>> tempo, beats_dense = librosa.beat.beat_track(y=y, sr=sr, sparse=False) >>> beats_dense array([False, False, False, True, False, False, False, False,

False, False, False, False, False, False, False, False, False, False, False, False, …, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False])

Track beats using a pre-computed onset envelope

>>> onset_env = librosa.onset.onset_strength(y=y, sr=sr,
...                                          aggregate=np.median)
>>> tempo, beats = librosa.beat.beat_track(onset_envelope=onset_env,
...                                        sr=sr)
>>> tempo
135.99917763157896
>>> beats
array([  3,  21,  40,  59,  78,  96, 116, 135, 154, 173, 192, 211,
       230, 249, 268, 287, 306, 325, 344, 363])

Plot the beat events against the onset strength envelope

>>> import matplotlib.pyplot as plt
>>> hop_length = 512
>>> fig, ax = plt.subplots(nrows=2, sharex=True)
>>> times = librosa.times_like(onset_env, sr=sr, hop_length=hop_length)
>>> M = librosa.feature.melspectrogram(y=y, sr=sr, hop_length=hop_length)
>>> librosa.display.specshow(librosa.power_to_db(M, ref=np.max),
...                          y_axis='mel', x_axis='time', hop_length=hop_length,
...                          ax=ax[0])
>>> ax[0].label_outer()
>>> ax[0].set(title='Mel spectrogram')
>>> ax[1].plot(times, librosa.util.normalize(onset_env),
...          label='Onset strength')
>>> ax[1].vlines(times[beats], 0, 1, alpha=0.5, color='r',
...            linestyle='--', label='Beats')
>>> ax[1].legend()
../_images/librosa-beat-beat_track-1.png