Cleanly isolating vocals from drums, bass, piano, and other musical accompaniment is the dream of every mashup artist, karaoke fan, and producer. Commercial solutions exist, but can be expensive and unreliable. Techniques like phase cancellation have very mixed results. The engineering team behind streaming music service Deezer just open-sourced Spleeter, their audio separation library built on Python and TensorFlow that uses machine learning to quickly and freely separate music into stems. The team at @Deezer just released #Spleeter, a Python music source separation library with state-of-the-art pre-trained models!

It is our great pleasure to announce the release of Open-unmix, a MIT-licensed python implementation for DNN-based music separation. In the recent years, deep learning-based systems could break a long-standing crystal ceiling, and finally allow high-quality music separation. However, until now, no open-source implementation was available that matches the performance of the best systems proposed more than four years ago. Not being able to reproduce state of the art performance makes it difficult to clearly identify the sources for discrepancies and rooms for improvement. In this context, we release Open-Unmix (UMX) as closing this gap by providing a reference implementation for DNN-based music separation.