MTG/DeepConvSep
Deep Convolutional Neural Networks for Musical Source Separation
This tool helps musicians, audio engineers, and researchers separate individual sound sources from a mixed music track. You provide an audio file, and it outputs separate audio files for elements like vocals, bass, drums, or specific instruments. This is useful for anyone needing to isolate components of a musical recording for analysis, remixing, or educational purposes.
483 stars. No commits in the last 6 months.
Use this if you need to cleanly extract distinct instrument or vocal tracks from a complete musical piece.
Not ideal if you are looking for a general-purpose audio editing suite or real-time separation for live performance.
Stars
483
Forks
109
Language
Python
License
AGPL-3.0
Category
Last pushed
Jan 31, 2020
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/MTG/DeepConvSep"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
pytorch/audio
Data manipulation and transformation for audio signal processing, powered by PyTorch
asteroid-team/asteroid
The PyTorch-based audio source separation toolkit for researchers
deezer/spleeter
Deezer source separation library including pretrained models.
audeering/opensmile
The Munich Open-Source Large-Scale Multimedia Feature Extractor
audeering/opensmile-python
Python package for openSMILE