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C++ library for audio and music analysis, description and synthesis, including Python bindings
The main features of mtg/essentia are: Computer Audition, Feature Extraction, Audio Processing and Analysis.
Open-source alternatives to mtg/essentia include: aubio/aubio — Aubio is an audio analysis and digital signal processing library designed for music information retrieval. It provides… yaafe/yaafe — Audio features extraction. jameslyons/python_speech_features — Python speech features is a signal processing toolkit and library for extracting standard speech recognition features… pannous/tensorflow-speech-recognition — This library provides a deep learning framework for training neural networks to perform speech recognition and audio… csteinmetz1/pyloudnorm — Flexible audio loudness meter in Python. cpjku/madmom — Python audio and music signal processing library.
Aubio is an audio analysis and digital signal processing library designed for music information retrieval. It provides a suite of tools for extracting musical features, estimating fundamental frequencies, and tracking rhythmic pulses in audio streams. The library specializes in the detection of pitch and beat, enabling the extraction of musical notes and the estimation of overall tempo. It also includes capabilities for automatic onset detection to identify the start of sonic events and the separation of audio signals into percussive transients and steady-state tonal components. The system c
Python speech features is a signal processing toolkit and library for extracting standard speech recognition features from raw audio signals. It provides computational capabilities to calculate mel-frequency cepstral coefficients, raw and log filterbank energies, and spectral subband centroids for automated speech recognition systems and acoustic analysis. The library implements audio signal transformations including pre-emphasis filtering, overlapping windowed frame segmentation, Fast Fourier Transform spectral analysis, mel-filterbank projection with configurable filter weights and frequenc
This library provides a deep learning framework for training neural networks to perform speech recognition and audio classification. It utilizes sequence-to-sequence architectures to map variable-length audio inputs into text or numerical outputs, enabling the development of custom speech-to-text transcription models. The project distinguishes itself through integrated audio processing capabilities that transform raw waveforms into spectrograms and high-dimensional numerical vectors. These tools allow for the extraction of unique vocal characteristics to identify speakers, as well as the clas
