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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')[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

1

Ellis, Daniel PW. “Beat tracking by dynamic programming.” Journal of New Music Research 36.1 (2007): 51-60. http://labrosa.ee.columbia.edu/projects/beattrack/

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

audio time series

srnumber > 0 [scalar]

sampling rate of y

onset_envelopenp.ndarray [shape=(n,)] 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]

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

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.

Returns
tempofloat [scalar, non-negative]

estimated global tempo (in beats per minute)

beatsnp.ndarray [shape=(m,)]

estimated beat event locations in the specified units (default is frame indices)

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.util.example_audio_file())

>>> tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
>>> tempo
64.599609375

Print the first 20 beat frames

>>> beats[:20]
array([ 320,  357,  397,  436,  480,  525,  569,  609,  658,
        698,  737,  777,  817,  857,  896,  936,  976, 1016,
       1055, 1095])

Or print them as timestamps

>>> librosa.frames_to_time(beats[:20], sr=sr)
array([  7.43 ,   8.29 ,   9.218,  10.124,  11.146,  12.19 ,
        13.212,  14.141,  15.279,  16.208,  17.113,  18.042,
        18.971,  19.9  ,  20.805,  21.734,  22.663,  23.591,
        24.497,  25.426])

Track beats using a pre-computed onset envelope

>>> onset_env = librosa.onset.onset_strength(y, sr=sr,
...                                          aggregate=np.median)
>>> tempo, beats = librosa.beat.beat_track(onset_envelope=onset_env,
...                                        sr=sr)
>>> tempo
64.599609375
>>> beats[:20]
array([ 320,  357,  397,  436,  480,  525,  569,  609,  658,
        698,  737,  777,  817,  857,  896,  936,  976, 1016,
       1055, 1095])

Plot the beat events against the onset strength envelope

>>> import matplotlib.pyplot as plt
>>> hop_length = 512
>>> plt.figure(figsize=(8, 4))
>>> times = librosa.times_like(onset_env, sr=sr, hop_length=hop_length)
>>> plt.plot(times, librosa.util.normalize(onset_env),
...          label='Onset strength')
>>> plt.vlines(times[beats], 0, 1, alpha=0.5, color='r',
...            linestyle='--', label='Beats')
>>> plt.legend(frameon=True, framealpha=0.75)
>>> # Limit the plot to a 15-second window
>>> plt.xlim(15, 30)
>>> plt.gca().xaxis.set_major_formatter(librosa.display.TimeFormatter())
>>> plt.tight_layout()
>>> plt.show()
../_images/librosa-beat-beat_track-1.png