.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_hprss.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here <sphx_glr_download_auto_examples_plot_hprss.py>`
to download the full example code
.. rst-class:: sphx-glr-example-title
.. _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>`_.
.. GENERATED FROM PYTHON SOURCE LINES 17-24
.. code-block:: default
import numpy as np
import matplotlib.pyplot as plt
import librosa
import librosa.display
.. GENERATED FROM PYTHON SOURCE LINES 25-26
Load an example clip with harmonics and percussives
.. GENERATED FROM PYTHON SOURCE LINES 26-29
.. code-block:: default
y, sr = librosa.load(librosa.ex('fishin'), duration=5, offset=10)
.. GENERATED FROM PYTHON SOURCE LINES 30-31
Compute the short-time Fourier transform of y
.. GENERATED FROM PYTHON SOURCE LINES 31-33
.. code-block:: default
D = librosa.stft(y)
.. GENERATED FROM PYTHON SOURCE LINES 34-37
Decompose D into harmonic and percussive components
:math:`D = D_\text{harmonic} + D_\text{percussive}`
.. GENERATED FROM PYTHON SOURCE LINES 37-40
.. code-block:: default
D_harmonic, D_percussive = librosa.decompose.hpss(D)
.. GENERATED FROM PYTHON SOURCE LINES 41-42
We can plot the two components along with the original spectrogram
.. GENERATED FROM PYTHON SOURCE LINES 42-63
.. code-block:: default
# Pre-compute a global reference power from the input spectrum
rp = np.max(np.abs(D))
fig, ax = plt.subplots(nrows=3, sharex=True, sharey=True)
img = librosa.display.specshow(librosa.amplitude_to_db(np.abs(D), ref=rp),
y_axis='log', x_axis='time', ax=ax[0])
ax[0].set(title='Full spectrogram')
ax[0].label_outer()
librosa.display.specshow(librosa.amplitude_to_db(np.abs(D_harmonic), ref=rp),
y_axis='log', x_axis='time', ax=ax[1])
ax[1].set(title='Harmonic spectrogram')
ax[1].label_outer()
librosa.display.specshow(librosa.amplitude_to_db(np.abs(D_percussive), ref=rp),
y_axis='log', x_axis='time', ax=ax[2])
ax[2].set(title='Percussive spectrogram')
fig.colorbar(img, ax=ax)
.. image-sg:: /auto_examples/images/sphx_glr_plot_hprss_001.png
:alt: Full spectrogram, Harmonic spectrogram, Percussive spectrogram
:srcset: /auto_examples/images/sphx_glr_plot_hprss_001.png
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 64-77
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
.. GENERATED FROM PYTHON SOURCE LINES 77-85
.. code-block:: default
# 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)
.. GENERATED FROM PYTHON SOURCE LINES 86-88
In the plots below, note that vibrato has been suppressed from the harmonic
components, and vocals have been suppressed in the percussive components.
.. GENERATED FROM PYTHON SOURCE LINES 88-125
.. code-block:: default
fig, ax = plt.subplots(nrows=5, ncols=2, sharex=True, sharey=True, figsize=(10, 10))
librosa.display.specshow(librosa.amplitude_to_db(np.abs(D_harmonic), ref=rp),
y_axis='log', x_axis='time', ax=ax[0, 0])
ax[0, 0].set(title='Harmonic')
librosa.display.specshow(librosa.amplitude_to_db(np.abs(D_percussive), ref=rp),
y_axis='log', x_axis='time', ax=ax[0, 1])
ax[0, 1].set(title='Percussive')
librosa.display.specshow(librosa.amplitude_to_db(np.abs(D_harmonic2), ref=rp),
y_axis='log', x_axis='time', ax=ax[1, 0])
librosa.display.specshow(librosa.amplitude_to_db(np.abs(D_percussive2), ref=rp),
y_axis='log', x_axis='time', ax=ax[1, 1])
librosa.display.specshow(librosa.amplitude_to_db(np.abs(D_harmonic4), ref=rp),
y_axis='log', x_axis='time', ax=ax[2, 0])
librosa.display.specshow(librosa.amplitude_to_db(np.abs(D_percussive4), ref=rp),
y_axis='log', x_axis='time', ax=ax[2, 1])
librosa.display.specshow(librosa.amplitude_to_db(np.abs(D_harmonic8), ref=rp),
y_axis='log', x_axis='time', ax=ax[3, 0])
librosa.display.specshow(librosa.amplitude_to_db(np.abs(D_percussive8), ref=rp),
y_axis='log', x_axis='time', ax=ax[3, 1])
librosa.display.specshow(librosa.amplitude_to_db(np.abs(D_harmonic16), ref=rp),
y_axis='log', x_axis='time', ax=ax[4, 0])
librosa.display.specshow(librosa.amplitude_to_db(np.abs(D_percussive16), ref=rp),
y_axis='log', x_axis='time', ax=ax[4, 1])
for i in range(5):
ax[i, 0].set(ylabel='margin={:d}'.format(2**i))
ax[i, 0].label_outer()
ax[i, 1].label_outer()
.. image-sg:: /auto_examples/images/sphx_glr_plot_hprss_002.png
:alt: Harmonic, Percussive
:srcset: /auto_examples/images/sphx_glr_plot_hprss_002.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 4.203 seconds)
.. _sphx_glr_download_auto_examples_plot_hprss.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_hprss.py <plot_hprss.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_hprss.ipynb <plot_hprss.ipynb>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_