.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_plot_featureprep_envclassifier.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_plot_featureprep_envclassifier.py:


=====================================
Feature Extraction for Classification
=====================================

Extract acoustic features from labeled data for 
training an environment or speech classifier.

To see how soundpy implements this, see `soundpy.builtin.envclassifier_feats`.


.. code-block:: default

    import os, sys
    import inspect
    currentdir = os.path.dirname(os.path.abspath(
        inspect.getfile(inspect.currentframe())))
    parentdir = os.path.dirname(currentdir)
    parparentdir = os.path.dirname(parentdir)
    packagedir = os.path.dirname(parparentdir)
    sys.path.insert(0, packagedir)

    import soundpy as sp 
    import IPython.display as ipd
    package_dir = '../../../'
    os.chdir(package_dir)
    sp_dir = package_dir


Prepare for Extraction: Data Organization
-----------------------------------------

I will use a sample speech commands data set:

Designate path relevant for accessing audiodata


.. code-block:: default

    data_dir = '{}../mini-audio-datasets/speech_commands/'.format(sp_dir)


Choose Feature Type 
~~~~~~~~~~~~~~~~~~~
We can extract 'mfcc', 'fbank', 'powspec', and 'stft'.
if you are working with speech, I suggest 'fbank', 'powspec', or 'stft'.


.. code-block:: default


    feature_type = 'fbank'


Set Duration of Audio 
~~~~~~~~~~~~~~~~~~~~~
How much audio in seconds used from each audio file.
The example noise and speech files are only 1 second long


.. code-block:: default

    dur_sec = 1


Built-In Functionality - soundpy extracts the features for you
---------------------------------------------------------------

Define which data to use and which features to extract
Everything else is based on defaults. A feature folder with
the feature data will be created in the current working directory.
(Although, you can set this under the parameter `data_features_dir`)
`visualize` saves periodic images of the features extracted.
This is useful if you want to know what's going on during the process.


.. code-block:: default

    extraction_dir = sp.envclassifier_feats(data_dir, 
                                              feature_type=feature_type, 
                                              dur_sec=dur_sec,
                                              visualize=True);


.. image:: /auto_examples/images/sphx_glr_plot_featureprep_envclassifier_001.png
    :alt: test FBANK features: label RIGHT
    :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    /home/airos/Projects/github/a-n-rose/Python-Sound-Tool/soundpy/files.py:352: UserWarning: Some files did not match those acceptable by this program. (i.e. non-audio files) The number of files not included: 4
      warnings.warn(message)
    /home/airos/Projects/github/a-n-rose/Python-Sound-Tool/soundpy/feats.py:2376: UserWarning: 
    WARNING: sample rate was not set. Setting it at 22050 Hz.
      warnings.warn(msg)
    /home/airos/Projects/github/a-n-rose/Python-Sound-Tool/soundpy/feats.py:2383: UserWarning: 
    WARNING: `win_size_ms` was not set. Setting it to 20 ms
      warnings.warn(msg)
    /home/airos/Projects/github/a-n-rose/Python-Sound-Tool/soundpy/feats.py:2390: UserWarning: 
    WARNING: `percent_overlap` was not set. Setting it to 0.5
      warnings.warn(msg)
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    Features saved at audiodata/example_feats_models/envclassifier/features_9m3d13h25m56s709ms/train_data.npy

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    Features saved at audiodata/example_feats_models/envclassifier/features_9m3d13h25m56s709ms/val_data.npy

    8% through test fbank feature extraction    16% through test fbank feature extraction    25% through test fbank feature extraction    33% through test fbank feature extraction    41% through test fbank feature extraction    50% through test fbank feature extraction    58% through test fbank feature extraction    66% through test fbank feature extraction    75% through test fbank feature extraction    83% through test fbank feature extraction    91% through test fbank feature extraction    100% through test fbank feature extraction
    Features saved at audiodata/example_feats_models/envclassifier/features_9m3d13h25m56s709ms/test_data.npy


    Finished! Total duration: 4.62 seconds.


The extracted features, extraction settings applied, and 
which audio files were assigned to which datasets
will be saved in the following directory:


.. code-block:: default

    extraction_dir


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    PosixPath('audiodata/example_feats_models/envclassifier/features_9m3d13h25m56s709ms')


Logged Information
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Let's have a look at the files in the extraction_dir. The files ending 
with .npy extension contain the feature data; the .csv files contain 
logged information. 


.. code-block:: default

    featfiles = list(extraction_dir.glob('*.*'))
    for f in featfiles:
        print(f.name)
  

.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    dict_encode.csv
    test_data.npy
    train_data.npy
    dataset_audio_assignments.csv
    log_extraction_settings.csv
    dataset_audiofiles.csv
    val_data.npy
    dict_decode.csv
    dict_encdodedlabel2audio.csv


Feature Settings
~~~~~~~~~~~~~~~~~~
Since much was conducted behind the scenes, it's nice to know how the features
were extracted, for example, the sample rate and number of frequency bins applied, etc.


.. code-block:: default

    feat_settings = sp.utils.load_dict(
        extraction_dir.joinpath('log_extraction_settings.csv'))
    for key, value in feat_settings.items():
        print(key, ' ---> ', value)
    
    
.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    dataset_dirs  --->  ['../../../../mini-audio-datasets/speech_commands/nine', '../../../../mini-audio-datasets/speech_commands/right', '../../../../mini-audio-datasets/speech_commands/right']
    feat_base_shape  --->  (99, 40)
    feat_model_shape  --->  (99, 41)
    complex_vals  --->  False
    context_window  --->  
    frames_per_sample  --->  
    labeled_data  --->  True
    decode_dict  --->  {0: 'nine', 1: 'right', 2: 'zero'}
    visualize  --->  True
    vis_every_n_frames  --->  50
    subsection_data  --->  False
    divide_factor  --->  5
    total_audiofiles  --->  150
    kwargs  --->  {'feature_type': 'fbank', 'dur_sec': 1, 'sr': 22050, 'win_size_ms': 20, 'percent_overlap': 0.5}


Labeled Data
~~~~~~~~~~~~~~~~~~
These are the labels and their encoded values:


.. code-block:: default

    encode_dict = sp.utils.load_dict(
        extraction_dir.joinpath('dict_encode.csv'))
    for key, value in encode_dict.items():
        print(key, ' ---> ', value)


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    nine  --->  0
    right  --->  1
    zero  --->  2


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

   **Total running time of the script:** ( 0 minutes  5.977 seconds)


.. _sphx_glr_download_auto_examples_plot_featureprep_envclassifier.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_featureprep_envclassifier.py <plot_featureprep_envclassifier.py>`


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

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


.. only:: html

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

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