.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_superflux.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_superflux.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_superflux.py:


================
Superflux onsets
================

This notebook demonstrates how to recover the Superflux onset detection algorithm of
`Boeck and Widmer, 2013 <http://dafx13.nuim.ie/papers/09.dafx2013_submission_12.pdf>`_
from librosa.

This algorithm improves onset detection accuracy in the presence of vibrato.

.. GENERATED FROM PYTHON SOURCE LINES 13-17

.. code-block:: Python


    # Code source: Brian McFee
    # License: ISC








.. GENERATED FROM PYTHON SOURCE LINES 18-19

We'll need numpy and matplotlib for this example

.. GENERATED FROM PYTHON SOURCE LINES 19-25

.. code-block:: Python

    import numpy as np
    import matplotlib.pyplot as plt

    import librosa
    import librosa.display








.. GENERATED FROM PYTHON SOURCE LINES 26-28

The method works fine for longer signals, but the
results are harder to visualize.

.. GENERATED FROM PYTHON SOURCE LINES 28-32

.. code-block:: Python

    y, sr = librosa.load(librosa.ex('trumpet', hq=True),
                         sr=44100)









.. GENERATED FROM PYTHON SOURCE LINES 33-34

These parameters are taken directly from the paper

.. GENERATED FROM PYTHON SOURCE LINES 34-43

.. code-block:: Python

    n_fft = 1024
    hop_length = int(librosa.time_to_samples(1./200, sr=sr))
    lag = 2
    n_mels = 138
    fmin = 27.5
    fmax = 16000.
    max_size = 3









.. GENERATED FROM PYTHON SOURCE LINES 44-46

The paper uses a log-frequency representation, but for
simplicity, we'll use a Mel spectrogram instead.

.. GENERATED FROM PYTHON SOURCE LINES 46-59

.. code-block:: Python

    S = librosa.feature.melspectrogram(y, sr=sr, n_fft=n_fft,
                                       hop_length=hop_length,
                                       fmin=fmin,
                                       fmax=fmax,
                                       n_mels=n_mels)


    fig, ax = plt.subplots()
    librosa.display.specshow(librosa.power_to_db(S, ref=np.max),
                             y_axis='mel', x_axis='time', sr=sr,
                             hop_length=hop_length, fmin=fmin, fmax=fmax, ax=ax)





.. image-sg:: /auto_examples/images/sphx_glr_plot_superflux_001.png
   :alt: plot superflux
   :srcset: /auto_examples/images/sphx_glr_plot_superflux_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 60-62

Now we'll compute the onset strength envelope and onset events
using the librosa defaults.

.. GENERATED FROM PYTHON SOURCE LINES 62-67

.. code-block:: Python

    odf_default = librosa.onset.onset_strength(y=y, sr=sr, hop_length=hop_length)
    onset_default = librosa.onset.onset_detect(y=y, sr=sr, hop_length=hop_length,
                                               units='time')









.. GENERATED FROM PYTHON SOURCE LINES 68-69

And similarly with the superflux method

.. GENERATED FROM PYTHON SOURCE LINES 69-80

.. code-block:: Python

    odf_sf = librosa.onset.onset_strength(S=librosa.power_to_db(S, ref=np.max),
                                          sr=sr,
                                          hop_length=hop_length,
                                          lag=lag, max_size=max_size)

    onset_sf = librosa.onset.onset_detect(onset_envelope=odf_sf,
                                          sr=sr,
                                          hop_length=hop_length,
                                          units='time')









.. GENERATED FROM PYTHON SOURCE LINES 81-87

If you look carefully, the default onset detector (top sub-plot) has
several false positives in high-vibrato regions, eg around 0.62s or
1.80s.

The superflux method (middle plot) is less susceptible to vibrato, and
does not detect onset events at those points.

.. GENERATED FROM PYTHON SOURCE LINES 87-112

.. code-block:: Python



    # sphinx_gallery_thumbnail_number = 2
    fig, ax = plt.subplots(nrows=3, sharex=True)

    frame_time = librosa.frames_to_time(np.arange(len(odf_default)),
                                        sr=sr,
                                        hop_length=hop_length)

    librosa.display.specshow(librosa.power_to_db(S, ref=np.max),
                             y_axis='mel', x_axis='time', sr=sr,
                             hop_length=hop_length, fmin=fmin, fmax=fmax, ax=ax[2])
    ax[2].set(xlim=[0, 5.0])


    ax[0].plot(frame_time, odf_default, label='Spectral flux')
    ax[0].vlines(onset_default, 0, odf_default.max(), label='Onsets')
    ax[0].legend()
    ax[0].label_outer()

    ax[1].plot(frame_time, odf_sf, color='g', label='Superflux')
    ax[1].vlines(onset_sf, 0, odf_sf.max(), label='Onsets')
    ax[1].legend()
    ax[0].label_outer()




.. image-sg:: /auto_examples/images/sphx_glr_plot_superflux_002.png
   :alt: plot superflux
   :srcset: /auto_examples/images/sphx_glr_plot_superflux_002.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.699 seconds)


.. _sphx_glr_download_auto_examples_plot_superflux.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_superflux.ipynb <plot_superflux.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

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


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_