The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …

Speech separation is the task of separating target speech from background interference.
Traditionally, speech separation is studied as a signal processing problem. A more recent …

Abstract We present the Open Graph Benchmark (OGB), a diverse set of challenging and
realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine …

In this work, we build upon our previous publication and use diffusion-based generative
models for speech enhancement. We present a detailed overview of the diffusion process …

In this paper, we propose a convolutional recurrent neural network for joint sound event
localization and detection (SELD) of multiple overlap** sound events in three-dimensional …

Spoken keyword spotting (KWS) deals with the identification of keywords in audio streams
and has become a fast-growing technology thanks to the paradigm shift introduced by deep …

A field that has directly benefited from the recent advances in deep learning is automatic
speech recognition (ASR). Despite the great achievements of the past decades, however, a …

Eliminating the negative effect of non-stationary environmental noise is a long-standing
research topic for automatic speech recognition but still remains an important challenge …

We present a neural network based approach to acoustic beamforming. The network is used
to estimate spectral masks from which the Cross-Power Spectral Density matrices of speech …

The CHiME challenges have played a significant role in the development and evaluation of
robust automatic speech recognition (ASR) systems. We introduce the CHiME-7 distant ASR …