librosa.util.peak_pick

librosa.util.peak_pick(x, *, pre_max, post_max, pre_avg, post_avg, delta, wait, sparse=True, axis=-1)

Use a flexible heuristic to pick peaks in a signal.

A sample n is selected as an peak if the corresponding x[n] fulfills the following three conditions:

1. x[n] == max(x[n - pre_max:n + post_max])

2. x[n] >= mean(x[n - pre_avg:n + post_avg]) + delta

3. n - previous_n > wait

where previous_n is the last sample picked as a peak (greedily).

This implementation is based on [1] and [2].

[1]

Boeck, Sebastian, Florian Krebs, and Markus Schedl. “Evaluating the Online Capabilities of Onset Detection Methods.” ISMIR. 2012.

[2]

https://github.com/CPJKU/onset_detection/blob/master/onset_program.py

Parameters:

xnp.ndarray

input signal to peak picks from

pre_maxint >= 0 [scalar]

number of samples before n over which max is computed

post_maxint >= 1 [scalar]

number of samples after n over which max is computed

pre_avgint >= 0 [scalar]

number of samples before n over which mean is computed

post_avgint >= 1 [scalar]

number of samples after n over which mean is computed

deltafloat >= 0 [scalar]

threshold offset for mean

waitint >= 0 [scalar]

number of samples to wait after picking a peak

sparsebool [scalar]

If True, the output are indices of detected peaks. If False, the output is a dense boolean array of the same shape as x.

axisint [scalar]

the axis over which to detect peaks.

Returns:

peaksnp.ndarray [shape=(n_peaks,) or shape=x.shape, dtype=int or bool]

indices of peaks in x (sparse=True) or a boolean array where peaks[…, n] indicates a peak at frame index n (sparse=False)

Raises:

ParameterError

If any input lies outside its defined range

Examples

>>> y, sr = librosa.load(librosa.ex('trumpet'))
>>> onset_env = librosa.onset.onset_strength(y=y, sr=sr,
...                                          hop_length=512,
...                                          aggregate=np.median)
>>> peaks = librosa.util.peak_pick(onset_env, pre_max=3, post_max=3, pre_avg=3, post_avg=5, delta=0.5, wait=10)
>>> peaks
array([  3,  27,  40,  61,  72,  88, 103])

Using dense output to make a boolean array of peak indicators >>> librosa.util.peak_pick(onset_env, pre_max=3, post_max=3, pre_avg=3, post_avg=5, … delta=0.5, wait=10, sparse=False) array([False, False, …, False, False])

>>> import matplotlib.pyplot as plt
>>> times = librosa.times_like(onset_env, sr=sr, hop_length=512)
>>> fig, ax = plt.subplots(nrows=2, sharex=True)
>>> D = np.abs(librosa.stft(y))
>>> librosa.display.specshow(librosa.amplitude_to_db(D, ref=np.max),
...                          y_axis='log', x_axis='time', ax=ax[1])
>>> ax[0].plot(times, onset_env, alpha=0.8, label='Onset strength')
>>> ax[0].vlines(times[peaks], 0,
...              onset_env.max(), color='r', alpha=0.8,
...              label='Selected peaks')
>>> ax[0].legend(frameon=True, framealpha=0.8)
>>> ax[0].label_outer()