Adaptive Reverberation Absorption using Non-stationary Masking Components Detection for Intelligibility Improvement

TL;DR

This paper introduces a novel time domain absorption method that leverages non-stationary components detection to improve speech intelligibility in noisy-reverberant environments without prior knowledge of speech or room characteristics.

Contribution

It presents a new adaptive reverberation absorption technique based on non-stationarity detection, enhancing speech intelligibility under challenging acoustic conditions.

Findings

Higher intelligibility improvements compared to existing methods

Effective detection of masking distortions using non-stationarity measures

No prior speech or room knowledge required for the method

Abstract

This letter proposes a new time domain absorption approach designed to reduce masking components of speech signals under noisy-reverberant conditions. In this method, the non-stationarity of corrupted signal segments is used to detect masking distortions based on a defined threshold. The nonstationarity is objectively measured and is also adopted to determine the absorption procedure. Additionally, no prior knowledge of speech statistics or of the room information is required for this technique. Three intelligibility measures (ESII, ASIIST, SRMRnorm) and a perceptual listening test are used for evaluation. The experiments results show that the proposed scheme leads to a higher intelligibility improvement when compared to competing methods.