.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_auto_examples/1_basic/plot_audio_representation.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_1_basic_plot_audio_representation.py>`
        to download the full example code

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

.. _sphx_glr__auto_examples_1_basic_plot_audio_representation.py:


Audio representation
====================

An audio signal can be represented in both, temporal and spectral domains. 
These representations are complementary and fundamental to understand the audio
signal characteristics. In this introductory example we will load an audio signal, 
apply basic transformations to better understand its features in time and frequency.

.. GENERATED FROM PYTHON SOURCE LINES 14-15

Load an audio file and plot the waveform

.. GENERATED FROM PYTHON SOURCE LINES 15-22

.. code-block:: Python

    import matplotlib.pyplot as plt
    from maad import sound
    from maad import util

    s, fs = sound.load('../../data/spinetail.wav')
    util.plot_wave(s, fs)
    plt.show()



.. image-sg:: /_auto_examples/1_basic/images/sphx_glr_plot_audio_representation_001.png
   :alt: plot audio representation
   :srcset: /_auto_examples/1_basic/images/sphx_glr_plot_audio_representation_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 23-26

It can be noticed that in this audio there are four consecutive songs of the spinetail 
*Cranioleuca erythorps*, every song lasting of approximatelly two seconds. 
Let's trim the signal to zoom in on the details of the song.

.. GENERATED FROM PYTHON SOURCE LINES 26-29

.. code-block:: Python


    s_trim = sound.trim(s, fs, 5, 8)








.. GENERATED FROM PYTHON SOURCE LINES 30-31

Onced trimmed, lets compute the envelope of the signal, the Fourier and short-time Fourier transforms.

.. GENERATED FROM PYTHON SOURCE LINES 31-35

.. code-block:: Python

    env = sound.envelope(s_trim, mode='fast', Nt=128)
    pxx, fidx = sound.spectrum(s, fs, nperseg=1024, method='welch')
    Sxx, tn, fn, ext = sound.spectrogram(s_trim, fs, window='hann', nperseg=1024, noverlap=512)








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Finally, we can visualize the signal characteristics in the temporal and 
spectral domains.

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.. code-block:: Python


    fig, ax = plt.subplots(4,1, figsize=(8,10))
    util.plot_wave(s_trim, fs, ax=ax[0])
    util.plot_wave(env, fs, ax=ax[1])
    util.plot_spectrum(pxx, fidx, ax=ax[2])
    util.plot_spectrogram(Sxx, extent=ext, ax=ax[3], colorbar=False)
    plt.show()



.. image-sg:: /_auto_examples/1_basic/images/sphx_glr_plot_audio_representation_002.png
   :alt: plot audio representation
   :srcset: /_auto_examples/1_basic/images/sphx_glr_plot_audio_representation_002.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.504 seconds)


.. _sphx_glr_download__auto_examples_1_basic_plot_audio_representation.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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