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Open-Unmix
OFFICIAL PAGE: Open-Unmix
According to their web page: "Open-Unmix, is a deep neural network reference implementation for music source separation, applicable for researchers, audio engineers and artists. Open-Unmix provides ready-to-use models that allow users to separate pop music into four stems: vocals, drums, bass and the remaining other instruments." "The model is available for three different frameworks. However, the pytorch implementation serves as the reference version that includes pre-trained networks trained on the MUSDB18 dataset." Open-Unmix is a command line application with no GUI (graphical user interface).
Image courtesy of Inria
ARTICLES:
Let’s try Open-Unmix, separate sound source of music (PyTorch) [wells12]
Open-Unmix: A Reference Implementation for Music Source Separation (introduction)
Open-Unmix: End-to-end music demixing with PyTorch [Devpost]
DEMO:
Open-Unmix: A Reference Implementation for Music Source Separation (demo-icassp)
VIDEOS:
Bob Dylan - Mr. Tambourine Man (harmonica removed) [sigsep]
Demo and Architecture - Audio Source Separation p.1 [Seth Adams]
Downloading Podcasts - Audio Source Separation p.2 [Seth Adams]
Open-Unmix Pytorch Summer Hackathon [sigsep]
Predicting Audio Sources - Audio Source Separation p.5 [Seth Adams]
Spleeter vs NVIDIA RTX Voice vs Open-Unmix (Best AI audio separator) [Spreadsheet Warrior]
Train Validation Splits - Audio Source Separation p.3 [Seth Adams]
Training Open-Unmix - Audio Source Separation p.4 [Seth Adams]
Use of deep learning to separate tabla from vocals [sameer sudame]