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 …

Models for audio source separation usually operate on the magnitude spectrum, which
ignores phase information and makes separation performance dependant on hyper …

Music source separation is the task of decomposing music into its constitutive components,
eg, yielding separated stems for the vocals, bass, and drums. Such a separation has many …

The discipline of Deep Learning has been recognized for its strong computational tools,
which have been extensively used in data and signal processing, with innumerable …

This paper reports the organization and results for the 2018 community-based Signal
Separation Evaluation Campaign (SiSEC 2018). This year's edition was focused on audio …

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 …

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 …

Music source separation performance has greatly improved in recent years with the advent
of approaches based on deep learning. Such methods typically require large amounts of …

Many people listen to recorded music as part of their everyday lives, eg, from radio or TV
programs, compact discs, downloads, or, increasingly, online streaming services …