# tyiannak/pyAudioAnalysis

Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications

Repository: https://github.com/tyiannak/pyAudioAnalysis
Canonical: https://ross.abutalabs.com/products/pyaudioanalysis
Language: Python
License: Apache-2.0
License Family: permissive
Topics: audio, machine-learning, signal-processing, audio-data, audio-analysis-tasks, pyaudioanalysis, python
Last push: 2025-08-04T16:44:35+00:00

## Health v2 (maintenance only)
Score: 48/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 35, release rhythm 35, longevity 100
- inputs: {"age_days": 4389, "days_push": 394, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6254, forks 1211 (observed 2026-08-28T04:09:41.044924+00:00)

## What it is
pyAudioAnalysis is a Python library for audio analysis covering feature extraction (MFCCs, spectrograms, chromagrams), supervised and unsupervised classification and segmentation, and regression. It also provides command-line tools for common audio analysis tasks like silence detection and speaker diarization.

## Use cases
- extract mfcc and spectrogram features from audio files
- train a classifier to distinguish music from speech
- detect audio events and remove silence from long recordings
- perform speaker diarization on a conversation recording
- build an audio emotion recognition model
- visualize audio data with dimensionality reduction
- classify unknown sounds into trained categories

## When to choose
- you need a broad toolkit of classical audio analysis features and ML pipelines in Python
- you want quick command-line tools for segmentation, silence removal, or diarization
- you are prototyping audio classification or regression with SVMs and similar models

## When to avoid
- you need deep-learning-based audio models rather than classical feature-based methods
- you require real-time streaming audio analysis in production
- you need GPU-accelerated or highly optimized industrial-grade audio processing

## Facets
- artifact type: library
- maturity: active
- function: audio-processing, machine-learning, nlp
- domain: machine-learning, speech-processing
- platform: python, cli, cross-platform
- tags: audio-analysis, feature-extraction, mfcc, speaker-diarization, audio-classification, signal-processing, svm, audio

## Member repositories
- tyiannak/pyAudioAnalysis (main) score 48

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:41.044924+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:46:35.783725+00:00, confidence not recorded.
  - readme: https://github.com/tyiannak/pyAudioAnalysis (fetched 2026-08-28T04:09:41.044924+00:00, sha 16255451e739)
  - registry_pypi: https://pypi.org/pypi/pyaudioanalysis/json (fetched 2026-08-29T08:43:11.922260+00:00, sha 9edcfc01ebad)
- Data as of 2026-08-30T08:39:29.467469+00:00.