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 …

We present RemixIT, a simple yet effective self-supervised method for training speech
enhancement without the need of a single isolated in-domain speech nor a noise waveform …

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 …

Singing, the vocal productionof musical tones, is one of the most important elements of
music. Addressing the needs of real-world applications, the study of technologies related to …

The wide application of deep learning techniques is boosting the regulation of deep learning
models, especially deep neural networks (DNN), as commercial products. A necessary …

We propose RemixIT, a simple and novel self-supervised training method for speech
enhancement. The proposed method is based on a continuously self-training scheme that …

Singing voice separation aims to separate music into vocals and accompaniment
components. One of the major constraints for the task is the limited amount of training data …

The collection of large amounts of labeled data for speech emotion recognition requires
considerable time and effort. As a result, the sizes of existing corpora are limited. One …

In this work, we propose Exformer, a time-domain architecture for target speaker extraction. It
consists of a pre-trained speaker embedder network and a separator network based on …

Learning effective vocal representations from a waveform mixture is a crucial but
challenging task for deep neural network (DNN)-based singing voice separation (SVS) …