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

TL;DR

This paper introduces a TAP-CRNN architecture that integrates a novel temporal attentive-pooling mechanism into convolutional recurrent networks to improve acoustic signal enhancement, especially in noisy environments like infant cry signals.

Contribution

The paper proposes a new TAP-CRNN model that considers both local and global attention for better noise reduction in acoustic signals, addressing limitations of previous architectures.

Findings

TAP-CRNN outperforms existing methods in noise reduction for infant cry signals.

The model effectively handles unseen background noises at low SNR levels.

It demonstrates significant improvement in signal clarity and noise suppression.

Abstract

In acoustic signal processing, the target signals usually carry semantic information, which is encoded in a hierarchal structure of short and long-term contexts. However, the background noise distorts these structures in a nonuniform way. The existing deep acoustic signal enhancement (ASE) architectures ignore this kind of local and global effect. To address this problem, we propose to integrate a novel temporal attentive-pooling (TAP) mechanism into a conventional convolutional recurrent neural network, termed as TAP-CRNN. The proposed approach considers both global and local attention for ASE tasks. Specifically, we first utilize a convolutional layer to extract local information of the acoustic signals and then a recurrent neural network (RNN) architecture is used to characterize temporal contextual information. Second, we exploit a novelattention mechanism to contextually process salient regions of the noisy signals. The proposed ASE system is evaluated using a benchmark infant cry dataset and compared with several well-known methods. It is shown that the TAPCRNN can more effectively reduce noise components from infant cry signals in unseen background noises at challenging signal-to-noise levels.