Home // ACHI 2020, The Thirteenth International Conference on Advances in Computer-Human Interactions // View article
Authors:
Yuhki Shiraishi
Takuma Takeda
Akihisa Shitara
Keywords: Alarm sound; Classification; Deaf and hard-of-hearing; Neural network; Smartphone
Abstract:
For the deaf and hard-of-hearing to be able to go out safely, it is important to recognize alarm sounds (horns, bicycle bells, ambulance sirens, etc.) among various environmental sounds and to transmit the kinds of sounds to those people, even in noisy environmental sounds. In this paper, we propose and develop an alarm sound classification system using deep neural networks by smartphones that can always be carried when they are going out. Besides, evaluation experiments are performed to verify the effectiveness of the system using the 5-fold cross-validation method. Furthermore, we evaluate the classification ratio for unlearned data and that adding the date download from the web, and also discuss the limitation of the system to improve the system more useful.
Pages: 30 to 33
Copyright: Copyright (c) IARIA, 2020
Publication date: March 22, 2020
Published in: conference
ISSN: 2308-4138
ISBN: 978-1-61208-761-0
Location: Valencia, Spain
Dates: from November 21, 2020 to November 25, 2020