.. _sphx_glr_auto_examples_plot_hprss.py:


=====================================
Harmonic-percussive source separation
=====================================

This notebook illustrates how to separate an audio signal into
its harmonic and percussive components.

We'll compare the original median-filtering based approach of
`Fitzgerald, 2010 <http://arrow.dit.ie/cgi/viewcontent.cgi?article=1078&context=argcon>`_
and its margin-based extension due to `Dreidger, Mueller and Disch, 2014
<http://www.terasoft.com.tw/conf/ismir2014/proceedings/T110_127_Paper.pdf>`_.





.. code-block:: python


    from __future__ import print_function
    import numpy as np
    import matplotlib.pyplot as plt

    import librosa
    import librosa.display







Load the example clip.



.. code-block:: python

    y, sr = librosa.load('audio/Karissa_Hobbs_-_09_-_Lets_Go_Fishin.mp3', offset=40, duration=10)








Compute the short-time Fourier transform of y



.. code-block:: python

    D = librosa.stft(y)







Decompose D into harmonic and percussive components

:math:`D = D_\text{harmonic} + D_\text{percussive}`



.. code-block:: python

    D_harmonic, D_percussive = librosa.decompose.hpss(D)








We can plot the two components along with the original spectrogram



.. code-block:: python


    # Pre-compute a global reference power from the input spectrum
    rp = np.max(np.abs(D))

    plt.figure(figsize=(12, 8))

    plt.subplot(3, 1, 1)
    librosa.display.specshow(librosa.amplitude_to_db(D, ref=rp), y_axis='log')
    plt.colorbar()
    plt.title('Full spectrogram')

    plt.subplot(3, 1, 2)
    librosa.display.specshow(librosa.amplitude_to_db(D_harmonic, ref=rp), y_axis='log')
    plt.colorbar()
    plt.title('Harmonic spectrogram')

    plt.subplot(3, 1, 3)
    librosa.display.specshow(librosa.amplitude_to_db(D_percussive, ref=rp), y_axis='log', x_axis='time')
    plt.colorbar()
    plt.title('Percussive spectrogram')
    plt.tight_layout()





.. image:: /auto_examples/images/sphx_glr_plot_hprss_001.png
    :align: center




The default HPSS above assigns energy to each time-frequency bin according to
whether a horizontal (harmonic) or vertical (percussive) filter responds higher
at that position.

This assumes that all energy belongs to either a harmonic or percussive source,
but does not handle "noise" well.  Noise energy ends up getting spread between
D_harmonic and D_percussive.

If we instead require that the horizontal filter responds more than the vertical
filter *by at least some margin*, and vice versa, then noise can be removed
from both components.

Note: the default (above) corresponds to margin=1



.. code-block:: python


    # Let's compute separations for a few different margins and compare the results below
    D_harmonic2, D_percussive2 = librosa.decompose.hpss(D, margin=2)
    D_harmonic4, D_percussive4 = librosa.decompose.hpss(D, margin=4)
    D_harmonic8, D_percussive8 = librosa.decompose.hpss(D, margin=8)
    D_harmonic16, D_percussive16 = librosa.decompose.hpss(D, margin=16)








In the plots below, note that vibrato has been suppressed from the harmonic
components, and vocals have been suppressed in the percussive components.



.. code-block:: python

    plt.figure(figsize=(10, 10))

    plt.subplot(5, 2, 1)
    librosa.display.specshow(librosa.amplitude_to_db(D_harmonic, ref=rp), y_axis='log')
    plt.title('Harmonic')
    plt.yticks([])
    plt.ylabel('margin=1')

    plt.subplot(5, 2, 2)
    librosa.display.specshow(librosa.amplitude_to_db(D_percussive, ref=rp), y_axis='log')
    plt.title('Percussive')
    plt.yticks([]), plt.ylabel('')

    plt.subplot(5, 2, 3)
    librosa.display.specshow(librosa.amplitude_to_db(D_harmonic2, ref=rp), y_axis='log')
    plt.yticks([])
    plt.ylabel('margin=2')

    plt.subplot(5, 2, 4)
    librosa.display.specshow(librosa.amplitude_to_db(D_percussive2, ref=rp), y_axis='log')
    plt.yticks([]) ,plt.ylabel('')

    plt.subplot(5, 2, 5)
    librosa.display.specshow(librosa.amplitude_to_db(D_harmonic4, ref=rp), y_axis='log')
    plt.yticks([])
    plt.ylabel('margin=4')

    plt.subplot(5, 2, 6)
    librosa.display.specshow(librosa.amplitude_to_db(D_percussive4, ref=rp), y_axis='log')
    plt.yticks([]), plt.ylabel('')

    plt.subplot(5, 2, 7)
    librosa.display.specshow(librosa.amplitude_to_db(D_harmonic8, ref=rp), y_axis='log')
    plt.yticks([])
    plt.ylabel('margin=8')

    plt.subplot(5, 2, 8)
    librosa.display.specshow(librosa.amplitude_to_db(D_percussive8, ref=rp), y_axis='log')
    plt.yticks([]), plt.ylabel('')

    plt.subplot(5, 2, 9)
    librosa.display.specshow(librosa.amplitude_to_db(D_harmonic16, ref=rp), y_axis='log')
    plt.yticks([])
    plt.ylabel('margin=16')

    plt.subplot(5, 2, 10)
    librosa.display.specshow(librosa.amplitude_to_db(D_percussive16, ref=rp), y_axis='log')
    plt.yticks([]), plt.ylabel('')

    plt.tight_layout()
    plt.show()



.. image:: /auto_examples/images/sphx_glr_plot_hprss_002.png
    :align: center




**Total running time of the script:** ( 0 minutes  4.695 seconds)



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     :download:`Download Python source code: plot_hprss.py <plot_hprss.py>`



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