From MFCCs to speaker diarization in one Python library

What it does

pyAudioAnalysis is a Python library that covers a wide range of audio analysis tasks. It extracts standard representations like MFCCs, spectrograms, and chromagrams; trains and evaluates segment classifiers; and detects events, strips silence, and segments recordings—including speaker diarization and audio thumbnail extraction. It also supports regression tasks such as emotion recognition and offers command-line access to all core functionality.

The interesting bit

The hook is breadth. You get feature extraction, visualization via dimensionality reduction, and diarization in one academic-backed package that has been citing its own PLOS ONE paper since 2015.

Key highlights

• Extracts MFCCs, spectrograms, and chromagrams.
• Trains classifiers and regression models (e.g., emotion recognition) with parameter tuning.
• Detects audio events, removes silence, and performs supervised and unsupervised segmentation.
• Generates audio thumbnails and visualizes content similarities.
• Provides both Python APIs and command-line interfaces for every task.

Verdict

Developers who need a broad audio analysis toolkit covering features, classification, and segmentation will find it useful. If you are after deep-learning architectures, the authors explicitly point to their separate deep_audio_features repository instead.