Similar to humans' cognitive ability to generalize knowledge and skills, self-supervised
learning (SSL) targets discovering general representations from large-scale data. This …

Deep neural networks (DNN) techniques have become pervasive in domains such as
natural language processing and computer vision. They have achieved great success in …

We present a method for simultaneously localizing multiple sound sources within a visual
scene. This task requires a model to both group a sound mixture into individual sources, and …

We introduce the Free Universal Sound Separation (FUSS) dataset, a new corpus for
experiments in separating mixtures of an unknown number of sounds from an open domain …

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 …

We present ESPnet-SE, which is designed for the quick development of speech
enhancement and speech separation systems in a single framework, along with the optional …

We introduce AudioScopeV2, a state-of-the-art universal audio-visual on-screen sound
separation system which is capable of learning to separate sounds and associate them with …

An automatic bird sound recognition system is a useful tool for collecting data of different
bird species for ecological analysis. Together with autonomous recording units (ARUs), such …

Recent progress in deep learning has enabled many advances in sound separation and
visual scene understanding. However, extracting sound sources which are apparent in …

This paper addresses the problem of species classification in bird song recordings. The
massive amount of available field recordings of birds presents an opportunity to use …