1 research outputs found
Speech Dereverberation Based on Integrated Deep and Ensemble Learning Algorithm
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