Popular music is often composed of an accompaniment and a lead component, the latter
typically consisting of vocals. Filtering such mixtures to extract one or both components has …

Source separation models either work on the spectrogram or waveform domain. In this work,
we show how to perform end-to-end hybrid source separation, letting the model decide …

The performance of music source separation (MSS) models has been greatly improved in
recent years thanks to the development of novel neural network architectures and training …

Source separation for music is the task of isolating contributions, or stems, from different
instruments recorded individually and arranged together to form a song. Such components …

Speech enhancement and automatic speech recognition (ASR) are most often evaluated in
matched (or multi-condition) settings where the acoustic conditions of the training data …

This paper deals with the separation of music into individual instrument tracks which is
known to be a challenging problem. We describe two different deep neural network …

Music source separation has been intensively studied in the last decade and tremendous
progress with the advent of deep learning could be observed. Evaluation campaigns such …

Deep neural networks have become an indispensable technique for audio source
separation (SS). It was recently reported that a variant of CNN architecture called MM …

In this paper, we report the results of the 2016 community-based Signal Separation
Evaluation Campaign (SiSEC 2016). This edition comprises four tasks. Three focus on the …

This paper deals with the problem of audio source separation. To handle the complex and ill-
posed nature of the problems of audio source separation, the current state-of-the-art …