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librosa.util.peak_pick

librosa.util.peak_pick(x, *, pre_max, post_max, pre_avg, post_avg, delta, wait, sparse=True, method='greedy', 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.

method{‘greedy’, ‘dp_count’, ‘dp_value’} [scalar]

The method used to pick peaks. The default is ‘greedy’, which implements the method of Böck et al. (2012). The greedy method selects the earliest possible peaks (i.e. those with minimal index values) subject to the constraints described above. The ‘dp_*’ methods implement a dynamic programming method which seeks to explicitly maximize either the number of selected peaks (dp_count) or the sum of the values x[p] the selected peaks p (dp_value).

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.loadx('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(D, vscale='dBFS',
...                          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()