.. _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.



.. code-block:: python


    # Code source: Brian McFee
    # License: ISC







We'll need numpy and matplotlib for this example



.. code-block:: python

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

    import librosa
    import librosa.display







We'll load in a five-second clip of a track that has
noticeable vocal vibrato.
The method works fine for longer signals, but the
results are harder to visualize.



.. code-block:: python

    y, sr = librosa.load('audio/Karissa_Hobbs_-_09_-_Lets_Go_Fishin.mp3',
                         sr=44100,
                         duration=5,
                         offset=35)








These parameters are taken directly from the paper



.. 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








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



.. 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)


    plt.figure(figsize=(6, 4))
    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)
    plt.tight_layout()





.. image:: /auto_examples/images/sphx_glr_plot_superflux_001.png
    :align: center




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



.. 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')








And similarly with the superflux method



.. 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')








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.



.. code-block:: python



    # sphinx_gallery_thumbnail_number = 2
    plt.figure(figsize=(6, 6))

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

    ax = plt.subplot(2, 1, 2)
    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)
    plt.xlim([0, 5.0])
    plt.axis('tight')


    plt.subplot(4, 1, 1, sharex=ax)
    plt.plot(frame_time, odf_default, label='Spectral flux')
    plt.vlines(onset_default, 0, odf_default.max(), label='Onsets')
    plt.xlim([0, 5.0])
    plt.legend()


    plt.subplot(4, 1, 2, sharex=ax)
    plt.plot(frame_time, odf_sf, color='g', label='Superflux')
    plt.vlines(onset_sf, 0, odf_sf.max(), label='Onsets')
    plt.xlim([0, 5.0])
    plt.legend()

    plt.tight_layout()
    plt.show()



.. image:: /auto_examples/images/sphx_glr_plot_superflux_002.png
    :align: center




**Total running time of the script:** ( 0 minutes  0.929 seconds)



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



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

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