A Novel Temporal Attentive-Pooling based Convolutional Recurrent Architecture for Acoustic Signal Enhancement

Abstract

Impact Statement -Recently proposed deep learning solutions have proven useful in overcoming certain limitations of conventional acoustic signal enhancement (ASE) tasks. However, the performance of these approaches under real acoustic conditions is not always satisfactory. In this study, we investigated the use of attention models for ASE. To the best of our knowledge, this is the first attempt to successfully employ a convolutional recurrent neural network (CRNN) with a temporal attentive pooling (TAP) algorithm for the ASE task. The proposed TAP-CRNN framework can practically benefit the assistive communication technology industry, such as the manufacture of hearing aid devices for the elderly and students. In addition, the derived algorithm can benefit other signal processing applications, such as soundscape information retrieval, sound environment analysis in smart homes, and automatic speech/speaker/language recognition systems.