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

56Citations
Citations of this article
48Readers
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.

References Powered by Scopus

Librispeech: An ASR corpus based on public domain audio books

5044Citations
N/AReaders
Get full text

Suppression of Acoustic Noise in Speech Using Spectral Subtraction

4057Citations
N/AReaders
Get full text

Speech Enhancement Using a Minimum Mean-Square Error Short-Time Spectral Amplitude Estimator

3212Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Fundamentals, present and future perspectives of speech enhancement

67Citations
N/AReaders
Get full text

Real-time speech enhancement using equilibriated RNN

48Citations
N/AReaders
Get full text

A Survey of Deep Learning on Mobile Devices: Applications, Optimizations, Challenges, and Research Opportunities

47Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

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

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 17

94%

Researcher 1

6%

Readers' Discipline

Tooltip

Engineering 12

55%

Computer Science 6

27%

Pharmacology, Toxicology and Pharmaceut... 2

9%

Medicine and Dentistry 2

9%

Save time finding and organizing research with Mendeley

Sign up for free