{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Feature Extraction for Classification\n\n\nExtract acoustic features from labeled data for \ntraining an environment or speech classifier.\n\nTo see how soundpy implements this, see `soundpy.builtin.envclassifier_feats`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import os, sys\nimport inspect\ncurrentdir = os.path.dirname(os.path.abspath(\n    inspect.getfile(inspect.currentframe())))\nparentdir = os.path.dirname(currentdir)\nparparentdir = os.path.dirname(parentdir)\npackagedir = os.path.dirname(parparentdir)\nsys.path.insert(0, packagedir)\n\nimport soundpy as sp \nimport IPython.display as ipd\npackage_dir = '../../../'\nos.chdir(package_dir)\nsp_dir = package_dir"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Prepare for Extraction: Data Organization\n-----------------------------------------\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "I will use a sample speech commands data set:\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Designate path relevant for accessing audiodata\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "data_dir = '{}../mini-audio-datasets/speech_commands/'.format(sp_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Choose Feature Type \n~~~~~~~~~~~~~~~~~~~\nWe can extract 'mfcc', 'fbank', 'powspec', and 'stft'.\nif you are working with speech, I suggest 'fbank', 'powspec', or 'stft'.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "feature_type = 'fbank'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Set Duration of Audio \n~~~~~~~~~~~~~~~~~~~~~\nHow much audio in seconds used from each audio file.\nThe example noise and speech files are only 1 second long\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "dur_sec = 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Built-In Functionality - soundpy extracts the features for you\n---------------------------------------------------------------\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Define which data to use and which features to extract\nEverything else is based on defaults. A feature folder with\nthe feature data will be created in the current working directory.\n(Although, you can set this under the parameter `data_features_dir`)\n`visualize` saves periodic images of the features extracted.\nThis is useful if you want to know what's going on during the process.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "extraction_dir = sp.envclassifier_feats(data_dir, \n                                          feature_type=feature_type, \n                                          dur_sec=dur_sec,\n                                          visualize=True);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The extracted features, extraction settings applied, and \nwhich audio files were assigned to which datasets\nwill be saved in the following directory:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "extraction_dir"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Logged Information\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nLet's have a look at the files in the extraction_dir. The files ending \nwith .npy extension contain the feature data; the .csv files contain \nlogged information. \n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "featfiles = list(extraction_dir.glob('*.*'))\nfor f in featfiles:\n    print(f.name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Feature Settings\n~~~~~~~~~~~~~~~~~~\nSince much was conducted behind the scenes, it's nice to know how the features\nwere extracted, for example, the sample rate and number of frequency bins applied, etc.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "feat_settings = sp.utils.load_dict(\n    extraction_dir.joinpath('log_extraction_settings.csv'))\nfor key, value in feat_settings.items():\n    print(key, ' ---> ', value)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Labeled Data\n~~~~~~~~~~~~~~~~~~\nThese are the labels and their encoded values:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "encode_dict = sp.utils.load_dict(\n    extraction_dir.joinpath('dict_encode.csv'))\nfor key, value in encode_dict.items():\n    print(key, ' ---> ', value)"
      ]
    }
  ],
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      "display_name": "Python 3",
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