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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/21700
Title: Emergency signal classification for the hearing impaired using multi-channel convolutional neural network architecture
Authors: Padhy S.
Tiwari J.
Rathore S.
Kumar, Neetesh Sharath
Published in: 2019 IEEE Conference on Information and Communication Technology, CICT 2019
2019 IEEE Conference on Information and Communication Technology, CICT 2019
Abstract: Hearing impaired people have to tackle a lot of challenges, particularly during emergencies, making them dependent on others. The presence of emergency situations is mostly comprehended through auditory means. This raises a need for developing such systems that sense emergency sounds and communicate it to the deaf effectively. The present study is conducted to differentiate emergency audio signals from non-emergency situations using Multi-Channel Convolutional Neural Networks (CNN). Various data augmentation techniques have been explored, with particular attention to the method of Mixup, in order to improve the performance of the model. The experimental results showed a cross-validation accuracy of 88.28 % and testing accuracy of 88.09 %. To put the model into practical lives of the hearing impaired an android application was developed that made the phone vibrate every time there was an emergency sound. The app could be connected to an android wear device such as a smartwatch that will be with the wearer every time, effectively making them aware of emergency situations. © 2019 IEEE.
Citation: 2019 IEEE Conference on Information and Communication Technology, CICT 2019 (2019)
URI: https://doi.org/10.1109/CICT48419.2019.9066252
http://repository.iitr.ac.in/handle/123456789/21700
Issue Date: 2019
Publisher: Institute of Electrical and Electronics Engineers Inc.
Keywords: Assistive technology
Audio data augmentation
Convolutional neural networks
Mel spectrograms
Mixup
Multi-channel
Sound classification
ISBN: 9.78173E+12
Author Scopus IDs: 57216695823
57216695224
57216694483
57207838186
Author Affiliations: Padhy, S., Information Technology, ABV-IIITM Gwalior, Madhya Pradesh, 474015, India
Tiwari, J., Information Technology, ABV-IIITM Gwalior, Madhya Pradesh, 474015, India
Rathore, S., Information Technology, ABV-IIITM Gwalior, Madhya Pradesh, 474015, India
Kumar, N., ABV-IIITM Gwalior, Department of Information Technology, Madhya Pradesh, 474015, India
Appears in Collections:Conference Publications [CS]

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