Various methods from optimisation and learning based models were introduced for the task of interference or bleeding reduction in live multi-track recordings.

Current state-of-the-art models, such as Facebook’s Hybrid Transformer Demucs and Band-split RNN, demonstrate strong performance on Western music but struggle with Indian classical music and out-of-domain instrument separation. This performance gap underscores the need for further research into developing source-agnostic and universally robust music source separation models.

A deep convolutional neural network-based architecture is trained to completely remove the music in a given music+speech audio for a other NLP task