Humans can listen to a target speaker even in challenging acoustic conditions that have
noise, reverberation, and interfering speakers. This phenomenon is known as the cocktail …

We introduce Wavesplit, an end-to-end source separation system. From a single mixture, the
model infers a representation for each source and then estimates each source signal given …

Deep learning approaches have recently achieved impressive performance on both audio
source separation and sound classification. Most audio source separation approaches focus …

We introduce the active audio-visual source separation problem, where an agent must move
intelligently in order to better isolate the sounds coming from an object of interest in its …

We propose a hierarchical meta-learning-inspired model for music source separation (Meta-
TasNet) in which a generator model is used to predict the weights of individual extractor …

Audio-visual segmentation is a challenging task that aims to predict pixel-level masks for
sound sources in a video. Previous work applied a comprehensive manually designed …

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 …

We propose a unified model for three inter-related tasks: 1) to\textit {separate} individual
sound sources from a mixed music audio, 2) to\textit {transcribe} each sound source to MIDI …

Universal source separation (USS) is a fundamental research task for computational
auditory scene analysis, which aims to separate mono recordings into individual source …

In recent years, music source separation has been one of the most intensively studied
research areas in music information retrieval. Improvements in deep learning lead to a big …