librosa.beat.tempo

librosa.beat.tempo(y=None, sr=22050, onset_envelope=None, hop_length=512, start_bpm=120, std_bpm=1.0, ac_size=8.0, max_tempo=320.0, aggregate=<function mean>)

Estimate the tempo (beats per minute)

Parameters:

y : np.ndarray [shape=(n,)] or None

audio time series

sr : number > 0 [scalar]

sampling rate of the time series

onset_envelope : np.ndarray [shape=(n,)]

pre-computed onset strength envelope

hop_length : int > 0 [scalar]

hop length of the time series

start_bpm : float [scalar]

initial guess of the BPM

std_bpm : float > 0 [scalar]

standard deviation of tempo distribution

ac_size : float > 0 [scalar]

length (in seconds) of the auto-correlation window

max_tempo : float > 0 [scalar, optional]

If provided, only estimate tempo below this threshold

aggregate : callable [optional]

Aggregation function for estimating global tempo. If None, then tempo is estimated independently for each frame.

Returns:

tempo : np.ndarray [scalar]

estimated tempo (beats per minute)

See also

librosa.onset.onset_strength, librosa.feature.tempogram

Notes

This function caches at level 30.

Examples

>>> # Estimate a static tempo
>>> y, sr = librosa.load(librosa.util.example_audio_file())
>>> onset_env = librosa.onset.onset_strength(y, sr=sr)
>>> tempo = librosa.beat.tempo(onset_envelope=onset_env, sr=sr)
>>> tempo
array([129.199])

>>> # Or a dynamic tempo
>>> dtempo = librosa.beat.tempo(onset_envelope=onset_env, sr=sr,
...                             aggregate=None)
>>> dtempo
array([ 143.555,  143.555,  143.555, ...,  161.499,  161.499,
        172.266])

Plot the estimated tempo against the onset autocorrelation

>>> import matplotlib.pyplot as plt
>>> # Convert to scalar
>>> tempo = np.asscalar(tempo)
>>> # Compute 2-second windowed autocorrelation
>>> hop_length = 512
>>> ac = librosa.autocorrelate(onset_env, 2 * sr // hop_length)
>>> freqs = librosa.tempo_frequencies(len(ac), sr=sr,
...                                   hop_length=hop_length)
>>> # Plot on a BPM axis.  We skip the first (0-lag) bin.
>>> plt.figure(figsize=(8,4))
>>> plt.semilogx(freqs[1:], librosa.util.normalize(ac)[1:],
...              label='Onset autocorrelation', basex=2)
>>> plt.axvline(tempo, 0, 1, color='r', alpha=0.75, linestyle='--',
...            label='Tempo: {:.2f} BPM'.format(tempo))
>>> plt.xlabel('Tempo (BPM)')
>>> plt.grid()
>>> plt.title('Static tempo estimation')
>>> plt.legend(frameon=True)
>>> plt.axis('tight')

Plot dynamic tempo estimates over a tempogram

>>> plt.figure()
>>> tg = librosa.feature.tempogram(onset_envelope=onset_env, sr=sr,
...                                hop_length=hop_length)
>>> librosa.display.specshow(tg, x_axis='time', y_axis='tempo')
>>> plt.plot(librosa.frames_to_time(np.arange(len(dtempo))), dtempo,
...          color='w', linewidth=1.5, label='Tempo estimate')
>>> plt.title('Dynamic tempo estimation')
>>> plt.legend(frameon=True, framealpha=0.75)

(Source code)