A reverberation-time-aware DNN approach leveraging spatial information for microphone array dereverberation

Abstract

A reverberation-time-aware deep-neural-network (DNN)-based multi-channel speech dereverberation framework is proposed to handle a wide range of reverberation times (RT60s). There are three key steps in designing a robust system. First, to accomplish simultaneous speech dereverberation and beamforming, we propose a framework, namely DNNSpatial, by selectively concatenating log-power spectral (LPS) input features of reverberant speech from multiple microphones in an array and map them into the expected output LPS features of anechoic reference speech based on a single deep neural network (DNN). Next, the temporal auto-correlation function of received signals at different RT60s is investigated to show that RT60-dependent temporal-spatial contexts in feature selection are needed in the DNNSpatial training stage in order to optimize the system performance in diverse reverberant environments. Finally, the RT60 is estimated to select the proper temporal and spatial contexts before feeding the log-power spectrum features to the trained DNNs for speech dereverberation. The experimental evidence gathered in this study indicates that the proposed framework outperforms the state-of-the-art signal processing dereverberation algorithm weighted prediction error (WPE) and conventional DNNSpatial systems without taking the reverberation time into account, even for extremely weak and severe reverberant conditions. The proposed technique generalizes well to unseen room size, array geometry and loudspeaker position, and is robust to reverberation time estimation error.

Tensor-To-Vector Regression for Multi-Channel Speech Enhancement Based on Tensor-Train Network

“…However, the DNN-based vector-to-vector regression, which is the focus in this work, mainly aims at single-channel speech enhancement and is not simply generalized to multi-channel speech enhancement. As shown in Figure 1, a traditional approach to dealing with an array of microphones is exploited spatial information at the input level by concatenating speech vectors from multiple microphones into a single high dimensional vector, e.g., [12,2]. Thus the vector-to-vector regression approach can still be employed for speech enhancement by appending multichannel feature vectors together into a high-dimensional vector and mapping it to a vector extracted from the reference vector.…”

Evolutionary Robot Calibration and Nonlinear Compensation Methodology Based on GA-DNN and an Extra Compliance Error Model

“…In this work, an improved calibration method, which takes into account the generalisation performance and robustness of geometric parameter correction, is introduced to enhance the essential positioning accuracy of robots. DNN has received substantial attention in both the signal processing field and the machine learning field with its strong regression capabilities [29][30][31]. e design of DNN architecture must be optimised to make the DNN demonstrate the best predictive capacity.…”