Speech Dereverberation Based on Integrated Deep and Ensemble Learning Algorithm

TL;DR

This paper introduces a novel integrated deep and ensemble learning algorithm (IDEA) for speech dereverberation, effectively improving the retrieval of clean speech signals from reverberant environments by combining multiple models.

Contribution

The study proposes a new integrated learning framework that combines multiple dereverberation models with a fusion function, enhancing performance over single-model approaches.

Findings

IDEA outperforms single deep-neural-network dereverberation models.

The method is effective in both matched and mismatched acoustic conditions.

Ensemble approach improves dereverberation accuracy and robustness.

Abstract

Reverberation, which is generally caused by sound reflections from walls, ceilings, and floors, can result in severe performance degradation of acoustic applications. Due to a complicated combination of attenuation and time-delay effects, the reverberation property is difficult to characterize, and it remains a challenging task to effectively retrieve the anechoic speech signals from reverberation ones. In the present study, we proposed a novel integrated deep and ensemble learning algorithm (IDEA) for speech dereverberation. The IDEA consists of offline and online phases. In the offline phase, we train multiple dereverberation models, each aiming to precisely dereverb speech signals in a particular acoustic environment; then a unified fusion function is estimated that aims to integrate the information of multiple dereverberation models. In the online phase, an input utterance is first processed by each of the dereverberation models. The outputs of all models are integrated accordingly to generate the final anechoic signal. We evaluated the IDEA on designed acoustic environments, including both matched and mismatched conditions of the training and testing data. Experimental results confirm that the proposed IDEA outperforms single deep-neural-network-based dereverberation model with the same model architecture and training data.