Spafe

Simplified Python Audio Features Extraction

Table of Contents

• Spafe
• Table of Contents
• Structure
  • Filter banks
  • Spectrograms
  • Features
  • Frequencies
  • Installation
    • Dependencies
    • Installation guide
      • Install from PyPI
      • Install from Anaconda
    • Install from source
  • Why use Spafe?
  • How to use
  • Contributing
  • Citing

Structure

spafe aims to simplify feature extractions frommono audiofiles. Spafe includes various computations related to filter banks, spectrograms, frequencies and cepstral features. The library has the following structure:

Filter banks

• Bark filter banks
• Gammatone filter banks
• Linear filter banks
• Mel filter banks

Spectrograms

• Bark spectrogram
• CQT spectrogram
• Erb spectrogram
• Mel spectrogram

Features

• Bark Frequency Cepstral Coefficients (BFCCs)
• Constant Q-transform Cepstral Coefficients (CQCCs)
• Gammatone Frequency Cepstral Coefficients (GFCCs)
• Linear Frequency Cepstral Coefficients (LFCCs)
• Linear Prediction Components (LPCs)
• Mel Frequency Cepstral Coefficients (MFCCs)
• Inverse Mel Frequency Cepstral Coefficients (IMFCCs)
• Magnitude based Spectral Root Cepstral Coefficients (MSRCCs)
• Normalized Gammachirp Cepstral Coefficients (NGCCs)
• Power-Normalized Cepstral Coefficients (PNCCs)
• Phase based Spectral Root Cepstral Coefficients (PSRCCs)
• Perceptual Linear Prediction Coefficents (PLPs)
• Rasta Perceptual Linear Prediction Coefficents (RPLPs)

The theory behind features computed using spafe can be summmarized in the following graph:

Frequencies

• Dominant frequencies
• Fundamental frequencies

Installation

Dependencies

spafe requires:

• Python(>= 3.5)
• NumPy(>= 1.22.0)
• SciPy(>= 1.8.0)

if you want to use the visualization module/ functions of spafe, you will need to install:

• Matplotlib(>= 3.5.2)

Installation guide

Once you have the Dependencies installed, use one of the following install options.

Install from PyPI

• To freshly install spafe:

pip install spafe

• To update an existing installation:

pip install -U spafe

Install from Anaconda

• Spafe is also available on anaconda:

conda install spafe

Install from source

• You can build spafe from source, by following:

git clone [email protected]:SuperKogito/spafe.git
cd spafe
python setup.py install

Why use Spafe?

Unlike most existing audio feature extraction libraries (python_speech_features,SpeechPy,surfboardandBob), Spafe provides more options for spectral features extraction algorithms, notably:

• Bark Frequency Cepstral Coefficients (BFCCs)
• Constant Q-transform Cepstral Coefficients (CQCCs)
• Gammatone Frequency Cepstral Coefficients (GFCCs)
• Power-Normalized Cepstral Coefficients (PNCCs)
• Phase based Spectral Root Cepstral Coefficients (PSRCCs)

Most existing libraries and to their credits provide great implementations for features extraction but are unfortunately limited to the Mel Frequency Features (MFCC) and at best have Bark frequency and linear predictive coefficients additionally.Librosafor example includes great implementation of various algorithms (only MFCC and LPC are included), based on theShort Time Fourrier Transform (STFT),which is theoretically more accurate but slower than theDiscret Fourrier Transform used in Spafe's implementation.

How to use

Various examples on how to use spafe are present in the documentationhttps://superkogito.github.io/spafe.

!Please make sure you are referring to the correct documentation version.

Contributing

Contributions are welcome and encouraged. To learn more about how to contribute to spafe please refer to theContributing guidelines

Citing

• If you want to cite spafe as a software, please cite the version used as indexed inZenodo:

  Ayoub Malek, Hadrien Titeux, Stefano Borzì, Christian Heider Nielsen, Fabian-Robert Stöter, Hervé Bredin, Eryk Urbański & Kevin Mattheus Moerman. (2023). SuperKogito/spafe: v0.3.3 (v0.3.3). Zenodo.https://doi.org/10.5281/zenodo.11396240

  

• You can also cite spafe's paper as follows:

  Malek, A., (2023). Spafe: Simplified python audio features extraction. Journal of Open Source Software, 8(81), 4739,https://doi.org/10.21105/joss.04739