The emerging field of artificial intelligence of things (AIoT, AI+ IoT) is driven by the
widespread use of intelligent infrastructures and the impressive success of deep learning …

The remarkable success of transformers in the field of natural language processing has
sparked the interest of the speech-processing community, leading to an exploration of their …

In this paper, we introduce the task of language-queried audio source separation (LASS),
which aims to separate a target source from an audio mixture based on a natural language …

Personalized speech enhancement (PSE) models utilize additional cues, such as speaker
embeddings like d-vectors, to remove background noise and interfering speech in real-time …

Deep learning models are increasingly deployed to edge devices for real-time applications.
To ensure stable service quality across diverse edge environments, it is highly desirable to …

This paper describes Tencent Ethereal Audio Lab–Northwestern Polytechnical University
personalized speech enhancement (TEA-PSE) system submitted to track 2 of the ICASSP …

Humans possess the remarkable ability to selectively attend to a single speaker amidst
competing voices and background noise, known as selective auditory attention. Recent …

This paper investigates how to improve the runtime speed of personalized speech
enhancement (PSE) networks while maintaining the model quality. Our approach includes …

Attractor-based end-to-end diarization is achieving comparable accuracy to the carefully
tuned conventional clustering-based methods on challenging datasets. However, the main …

A method to perform offline and online speaker diarization for an unlimited number of
speakers is described in this paper. End-to-end neural diarization (EEND) has achieved …