Forward Convolutive Prediction for Frame Online Monaural Speech Dereverberation Based on Kronecker Product Decomposition

TL;DR

This paper introduces a novel forward convolutive prediction method using Kronecker product decomposition to efficiently perform monaural speech dereverberation with reduced computational complexity.

Contribution

It proposes a new FCP approach that models long prediction filters as Kronecker products of shorter filters, enabling online adaptive dereverberation with lower computational cost.

Findings

Achieves comparable dereverberation performance to traditional methods.

Substantially reduces computational complexity.

Demonstrates effectiveness in online adaptive scenarios.

Abstract

Dereverberation has long been a crucial research topic in speech processing, aiming to alleviate the adverse effects of reverberation in voice communication and speech interaction systems. Among existing approaches, forward convolutional prediction (FCP) has recently attracted attention. It typically employs a deep neural network to predict the direct-path signal and subsequently estimates a linear prediction filter to suppress residual reverberation. However, a major drawback of this approach is that the required linear prediction filter is often excessively long, leading to considerable computational complexity. To address this, our work proposes a novel FCP method based on Kronecker product (KP) decomposition, in which the long prediction filter is modeled as the KP of two much shorter filters. This decomposition significantly reduces the computational cost. An adaptive algorithm is then provided to iteratively update these shorter filters online. Experimental results show that, compared to conventional methods, our approach achieves competitive dereverberation performance while substantially reducing computational cost.