A Real-Time Convolutional Neural Network Based Speech Enhancement for Hearing Impaired Listeners Using Smartphone

54Citations
Citations of this article
47Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

This paper presents a Speech Enhancement (SE) technique based on multi-objective learning convolutional neural network to improve the overall quality of speech perceived by Hearing Aid (HA) users. The proposed method is implemented on a smartphone as an application that performs real-Time SE. This arrangement works as an assistive tool to HA. A multi-objective learning architecture including primary and secondary features uses a mapping-based convolutional neural network (CNN) model to remove noise from a noisy speech spectrum. The algorithm is computationally fast and has a low processing delay which enables it to operate seamlessly on a smartphone. The steps and the detailed analysis of real-Time implementation are discussed. The proposed method is compared with existing conventional and neural network-based SE techniques through speech quality and intelligibility metrics in various noisy speech conditions. The key contribution of this paper includes the realization of CNN SE model on a smartphone processor that works seamlessly with HA. The experimental results demonstrate significant improvements over the state-of-The-Art techniques and reflect the usability of the developed SE application in noisy environments.

Cite

CITATION STYLE

APA

Bhat, G. S., Shankar, N., Reddy, C. K. A., & Panahi, I. M. S. (2019). A Real-Time Convolutional Neural Network Based Speech Enhancement for Hearing Impaired Listeners Using Smartphone. IEEE Access, 7, 78421–78433. https://doi.org/10.1109/ACCESS.2019.2922370

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free