A natural question arising in Music Source Separation (MSS) is whether long range
contextual information is useful, or whether local acoustic features are sufficient. In other …

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 …

Self-supervised learning (SSL) has recently emerged as a promising paradigm for training
generalisable models on large-scale data in the fields of vision, text, and speech. Although …

In the era of extensive intersection between art and Artificial Intelligence (AI), such as image
generation and fiction co-creation, AI for music remains relatively nascent, particularly in …

The rise of singing voice synthesis presents critical challenges to artists and industry
stakeholders over unauthorized voice usage. Unlike synthesized speech, synthesized …

This paper summarizes the music demixing (MDX) track of the Sound Demixing Challenge
(SDX'23). We provide a summary of the challenge setup and introduce the task of robust …

Music source separation (MSS) aims to separate a music recording into multiple musically
distinct stems, such as vocals, bass, drums, and more. Recently, deep learning approaches …

Music mixing traditionally involves recording instruments in the form of clean, individual
tracks and blending them into a final mixture using audio effects and expert knowledge (eg …

In this work, we address the novel and rarely considered source separation task of
decomposing piano concerto recordings into separate piano and orchestral tracks. Being a …