In recent years, foundation models (FMs) such as large language models (LLMs) and latent
diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This …

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

A comprehensive assessment of ecosystem dynamics requires the monitoring of biological,
physical and social changes. Changes that cannot be observed visually may be trackable …

Learning how objects sound from video is challenging, since they often heavily overlap in a
single audio channel. Current methods for visually-guided audio source separation sidestep …

We study the problem of source separation for music using deep learning with four known
sources: drums, bass, vocals and other accompaniments. State-of-the-art approaches …

Many audio processing tasks require perceptual assessment. The``gold standard``of
obtaining human judgments is time-consuming, expensive, and cannot be used as an …

La séparation de sources sonores consiste à extraire une ou plusieurs sources présentant
un attrait significatif d'un enregistrement contenant plusieurs sources sonores. Ces …

In many situations, we would like to hear desired sound events (SEs) while being able to
ignore interference. Target sound extraction (TSE) tackles this problem by estimating the …

Source separation is the task of separating an audio recording into individual sound
sources. Source separation is fundamental for computational auditory scene analysis …

One of the frontier issues that severely hamper the development of automatic snore sound
classification (ASSC) associates to the lack of sufficient supervised training data. To cope …