Scaling text-to-speech (TTS) to large-scale, multi-speaker, and in-the-wild datasets is
important to capture the diversity in human speech such as speaker identities, prosodies …

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 …

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 …

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 …

In this work, we define a diffusion-based generative model capable of both music synthesis
and source separation by learning the score of the joint probability density of sources …

This paper describes our submission to the L3DAS22 Challenge Task 1, which consists of
speech enhancement with 3D Ambisonic microphones. The core of our approach combines …

TorchAudio is an open-source audio and speech processing library built for PyTorch. It aims
to accelerate the research and development of audio and speech technologies by providing …

Automatic speech recognition (ASR) has progressed significantly in recent years due to the
emergence of large-scale datasets and the self-supervised learning (SSL) paradigm …

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 …

We present AERO, a audio super-resolution model that processes speech and music
signals in the spectral domain. AERO is based on an encoder-decoder architecture with …