Spafe
Spafe: Simplified python audio features extraction - Published in JOSS (2023)
Science Score: 93.0%
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○CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 17 DOI reference(s) in README and JOSS metadata -
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✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords
Keywords from Contributors
Scientific Fields
Repository
:sound: spafe: Simplified Python Audio Features Extraction
Basic Info
- Host: GitHub
- Owner: SuperKogito
- License: bsd-3-clause
- Language: Python
- Default Branch: master
- Homepage: https://superkogito.github.io/spafe/
- Size: 20.7 MB
Statistics
- Stars: 477
- Watchers: 11
- Forks: 79
- Open Issues: 0
- Releases: 8
Topics
Metadata Files
README.md
Spafe
Simplified Python Audio Features Extraction
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Table of Contents
Structure
spafe aims to simplify feature extractions from mono audio files.
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:
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 git@github.com:SuperKogito/spafe.git cd spafe python setup.py install
Why use Spafe?
Unlike most existing audio feature extraction libraries (pythonspeechfeatures, SpeechPy, surfboard and Bob), 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. Librosa for example includes great implementation of various algorithms (only MFCC and LPC are included), based on the Short Time Fourrier Transform (STFT), which is theoretically more accurate but slower than the Discret Fourrier Transform used in Spafe's implementation.
How to use
Various examples on how to use spafe are present in the documentation https://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 the Contributing guidelines
Citing
- If you want to cite spafe as a software, please cite the version used as indexed in Zenodo:
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
Owner
- Name: Ayoub Malek
- Login: SuperKogito
- Kind: user
- Location: Munich
- Company: @Huawei
- Website: https://superkogito.github.io/
- Repositories: 36
- Profile: https://github.com/SuperKogito
MSc. in EE & IT from TUM, ML engineer, programming enthusiast and coffee addict.
JOSS Publication
Spafe: Simplified python audio features extraction
Authors
Yoummday GmbH
Editor
Fabian-Robert Stöter
Tags
Signal processing time-frequency analysis audio features extractionGitHub Events
Total
- Watch event: 24
- Push event: 2
- Pull request review event: 2
- Pull request event: 2
- Fork event: 1
Last Year
- Watch event: 24
- Push event: 2
- Pull request review event: 2
- Pull request event: 2
- Fork event: 1
Committers
Last synced: 5 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| SuperKogito | k****o@h****r | 319 |
| hadware | h****z@g****m | 11 |
| Stefano Borzì | s****2@g****m | 3 |
| Ayoub | a****b@a****x | 2 |
| eryk-urbanski | e****4@g****m | 1 |
| dependabot[bot] | 4****] | 1 |
| chomoska | d****n@g****m | 1 |
| Kevin Mattheus Moerman | K****n | 1 |
| Hervé BREDIN | h****n | 1 |
| Fabian-Robert Stöter | f****t | 1 |
| Christian Heider Nielsen | 5****r | 1 |
| Jan Reimes | j****s@h****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 46
- Total pull requests: 24
- Average time to close issues: 11 months
- Average time to close pull requests: 22 days
- Total issue authors: 28
- Total pull request authors: 11
- Average comments per issue: 1.72
- Average comments per pull request: 0.71
- Merged pull requests: 22
- Bot issues: 0
- Bot pull requests: 1
Past Year
- Issues: 0
- Pull requests: 1
- Average time to close issues: N/A
- Average time to close pull requests: 5 days
- Issue authors: 0
- Pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- SuperKogito (11)
- Helias (3)
- JJun-Guo (3)
- kabrate (2)
- ngragaei (2)
- shakeel608 (2)
- hadware (2)
- chmod740 (1)
- ruaruaruabick (1)
- rbsingh13 (1)
- xuke57 (1)
- weixiu00 (1)
- www516717402 (1)
- 0viAB (1)
- Cybernorse (1)
Pull Request Authors
- SuperKogito (11)
- Helias (7)
- hadware (2)
- chomoska (2)
- eryk-urbanski (2)
- jr2804 (1)
- dependabot[bot] (1)
- hbredin (1)
- Kevin-Mattheus-Moerman (1)
- cnheider (1)
- faroit (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 2
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Total downloads:
- pypi 10,074 last-month
-
Total dependent packages: 1
(may contain duplicates) -
Total dependent repositories: 9
(may contain duplicates) - Total versions: 16
- Total maintainers: 1
pypi.org: spafe
Simplified Python Audio-Features Extraction.
- Homepage: https://github.com/SuperKogito/spafe
- Documentation: https://spafe.readthedocs.io/
- License: BSD
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Latest release: 0.3.3
published over 1 year ago
Rankings
Maintainers (1)
proxy.golang.org: github.com/superkogito/spafe
- Documentation: https://pkg.go.dev/github.com/superkogito/spafe#section-documentation
- License: bsd-3-clause
-
Latest release: v0.3.3
published over 1 year ago
Rankings
Dependencies
- actions/checkout v2 composite
- actions/setup-python v2 composite
- actions/upload-artifact v1 composite
- openjournals/openjournals-draft-action master composite
- numpy >=1.21
- scipy >=1.7.3
- typing_extensions *