Recent studies in deep learning-based speech separation have proven the superiority of
time-domain approaches to conventional time-frequency-based methods. Unlike the time …

Most existing datasets for sound event recognition (SER) are relatively small and/or domain-
specific, with the exception of AudioSet, based on over 2 M tracks from YouTube videos and …

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

For challenging acoustic scenarios as low signal-to-noise ratios, current speech
enhancement systems usually suffer from performance bottleneck in extracting the target …

One of the most amazing functions of the human auditory system is the ability to detect all
kinds of sound events in the environment. With the technologies and hardware advances …

In recent years, rapid progress has been made on the problem of single-channel sound
separation using supervised training of deep neural networks. In such supervised …

Language-queried audio source separation (LASS) is a new paradigm for computational
auditory scene analysis (CASA). LASS aims to separate a target sound from an audio …

In this paper, we present an efficient neural network for end-to-end general purpose audio
source separation. Specifically, the backbone structure of this convolutional network is the …

This paper describes Asteroid, the PyTorch-based audio source separation toolkit for
researchers. Inspired by the most successful neural source separation systems, it provides …

This paper introduces a dual-signal transformation LSTM network (DTLN) for real-time
speech enhancement as part of the Deep Noise Suppression Challenge (DNS-Challenge) …