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librosa.decompose.hpss(S, *, kernel_size=31, power=2.0, mask=False, margin=1.0)[source]

Median-filtering harmonic percussive source separation (HPSS).

If margin = 1.0, decomposes an input spectrogram S = H + P where H contains the harmonic components, and P contains the percussive components.

If margin > 1.0, decomposes an input spectrogram S = H + P + R where R contains residual components not included in H or P.

This implementation is based upon the algorithm described by [1] and [2].

Snp.ndarray [shape=(…, d, n)]

input spectrogram. May be real (magnitude) or complex. Multi-channel is supported.

kernel_sizeint or tuple (kernel_harmonic, kernel_percussive)

kernel size(s) for the median filters.

  • If scalar, the same size is used for both harmonic and percussive.

  • If tuple, the first value specifies the width of the harmonic filter, and the second value specifies the width of the percussive filter.

powerfloat > 0 [scalar]

Exponent for the Wiener filter when constructing soft mask matrices.


Return the masking matrices instead of components.

Masking matrices contain non-negative real values that can be used to measure the assignment of energy from S into harmonic or percussive components.

Components can be recovered by multiplying S * mask_H or S * mask_P.

marginfloat or tuple (margin_harmonic, margin_percussive)

margin size(s) for the masks (as described in [2])

  • If scalar, the same size is used for both harmonic and percussive.

  • If tuple, the first value specifies the margin of the harmonic mask, and the second value specifies the margin of the percussive mask.

harmonicnp.ndarray [shape=(…, d, n)]

harmonic component (or mask)

percussivenp.ndarray [shape=(…, d, n)]

percussive component (or mask)


This function caches at level 30.


Separate into harmonic and percussive

>>> y, sr = librosa.load(librosa.ex('choice'), duration=5)
>>> D = librosa.stft(y)
>>> H, P = librosa.decompose.hpss(D)
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(nrows=3, sharex=True, sharey=True)
>>> img = librosa.display.specshow(librosa.amplitude_to_db(np.abs(D),
...                                                        ref=np.max),
...                          y_axis='log', x_axis='time', ax=ax[0])
>>> ax[0].set(title='Full power spectrogram')
>>> ax[0].label_outer()
>>> librosa.display.specshow(librosa.amplitude_to_db(np.abs(H),
...                                                  ref=np.max(np.abs(D))),
...                          y_axis='log', x_axis='time', ax=ax[1])
>>> ax[1].set(title='Harmonic power spectrogram')
>>> ax[1].label_outer()
>>> librosa.display.specshow(librosa.amplitude_to_db(np.abs(P),
...                                                  ref=np.max(np.abs(D))),
...                          y_axis='log', x_axis='time', ax=ax[2])
>>> ax[2].set(title='Percussive power spectrogram')
>>> fig.colorbar(img, ax=ax, format='%+2.0f dB')

Or with a narrower horizontal filter

>>> H, P = librosa.decompose.hpss(D, kernel_size=(13, 31))

Just get harmonic/percussive masks, not the spectra

>>> mask_H, mask_P = librosa.decompose.hpss(D, mask=True)
>>> mask_H
array([[1.853e-03, 1.701e-04, ..., 9.922e-01, 1.000e+00],
       [2.316e-03, 2.127e-04, ..., 9.989e-01, 1.000e+00],
       [8.195e-05, 6.939e-05, ..., 3.105e-04, 4.231e-04],
       [3.159e-05, 4.156e-05, ..., 6.216e-04, 6.188e-04]],
>>> mask_P
array([[9.981e-01, 9.998e-01, ..., 7.759e-03, 3.201e-05],
       [9.977e-01, 9.998e-01, ..., 1.122e-03, 4.451e-06],
       [9.999e-01, 9.999e-01, ..., 9.997e-01, 9.996e-01],
       [1.000e+00, 1.000e+00, ..., 9.994e-01, 9.994e-01]],

Separate into harmonic/percussive/residual components by using a margin > 1.0

>>> H, P = librosa.decompose.hpss(D, margin=3.0)
>>> R = D - (H+P)
>>> y_harm = librosa.istft(H)
>>> y_perc = librosa.istft(P)
>>> y_resi = librosa.istft(R)

Get a more isolated percussive component by widening its margin

>>> H, P = librosa.decompose.hpss(D, margin=(1.0,5.0))