Speech restoration aims to remove distortions in speech signals. Prior methods mainly focus
on a single type of distortion, such as speech denoising or dereverberation. However …

Speech enhancement aims to improve the listening quality and intelligibility of noisy speech
in adverse environments. It proves to be challenging to perform speech enhancement in …

As a fundamental problem in speech processing, pitch tracking has been studied for
decades. While strong performance has been achieved on clean speech, pitch tracking in …

Bone-conduction (BC) microphones capture speech signals by converting the vibrations of
the human skull into electrical signals. BC sensors are insensitive to acoustic noise, but …

This paper proposes a neural cascade architecture to address the monaural speech
enhancement problem. The cascade architecture is composed of three modules which …

Modern neural speech enhancement models usually include various forms of phase
information in their training loss terms, either explicitly or implicitly. However, these loss …

It is known that the reverberant speech in different acoustic environments varies according to
reverberation time. However, most deep learning based speech dereverberation methods …

Deep learning has led to dramatic performance improvements for the task of speech
enhancement, where deep neural networks (DNNs) are trained to recover clean speech …

This article addresses the multi-channel linear prediction (MCLP)-based speech
dereverberation problem by jointly considering the sparsity and low-rank priors of speech …

Current supervised speech enhancement methods based on deep learning typically utilize
amplitude-based loss functions for optimization, such as Mean Absolute Error (MAE) or …