Design and Integration of Alert Signal Detector and Separator for Hearing Aid Applications

Abstract

Alert signals like sirens and home alarms are important as they warn people of precarious situations. This work presents the detection and separation of these acoustically important alert signals, not to be attenuated as noise, to assist the hearing impaired listeners. The proposed method is based on convolutional neural network (CNN) and convolutional-recurrent neural network (CRNN). The developed method consists of two blocks, the detector block, and the separator block. The entire setup is integrated with speech enhancement (SE) algorithms, and before the compression stage, used in a hearing aid device (HAD) signal processing pipeline. The detector recognizes the presence of alert signal in various noisy environments. The separator block separates the alert signal from the mixture of noisy signals before passing it through SE to ensure minimal or no attenuation of the alert signal. It is implemented on a smartphone as an application that seamlessly works with HADs in real-time. This smartphone assistive setup allows the hearing aid users to know the presence of the alert sounds even when these are out of sight. The algorithm is computationally efficient with a low processing delay. The key contribution of this paper includes the development and integration of alert signal separator block with SE and the realization of the entire setup on a smartphone in real-time. The proposed method is compared with several state-of-the-art techniques through objective measures in various noisy conditions. The experimental analysis demonstrates the effectiveness and practical usefulness of the developed setup in real-world noisy scenarios.

Keywords

• Alert signals
• convolutional-recurrent neural networks (CRNN)
• detection
• separation
• speech enhancement (SE)
• hearing aid (HA)