Sound classification using deep learning for hard of hearing and deaf people

Sound classification using deep learning for hard of hearing and deaf people

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.

Bibliographic Details
Main Authors: Habib, Md.Adnan, Arefeen, Zarif Raiyan, Hussain, Arafat, Shahriyer, S.M.Rownak, Islam, Tanzid
Other Authors: Parvez, Mohammad Zavid
Format: Thesis
Language:English
Published: Brac University 2022
Subjects:
RNN
CNN
melspectrogram
Audio feature extraction
Neural networks (Computer science)
Online Access:http://hdl.handle.net/10361/16565
id 10361-16565
record_format dspace
spelling 10361-165652022-05-11T04:02:40Z Sound classification using deep learning for hard of hearing and deaf people Habib, Md.Adnan Arefeen, Zarif Raiyan Hussain, Arafat Shahriyer, S.M.Rownak Islam, Tanzid Parvez, Mohammad Zavid Rahman, Rafeed Department of Computer Science and Engineering, Brac University RNN CNN melspectrogram Audio feature extraction Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 35-37). Our paper mainly focuses on developing an audio classification for people, who cannot hear properly, using Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). One of the many prevalent complaints from hearing aid users is excessive background noise. Hearing aids with background noise classification algorithms can modify the response based on the noisy environment. Speech, azan, and ambient noises are all examples of significant audio signals. Whenever a human hears a sound, they can easily identify the sound, however it’s not the same for computers, and we have to feed the algorithm data-sets in order to make it distinguish between different sounds[1]. Hence, we came up with the idea to build a system for people who have problems to hear. We have successfully managed to achieve a total of 98.67%, and 97.01% accuracy after training the data on our CNN and RNN model and testing it respectively. Md.Adnan Habib Zarif Raiyan Arefeen Arafat Hussain S.M.Rownak Shahriyer Tanzid Islam B. Computer Science 2022-04-25T04:33:40Z 2022-04-25T04:33:40Z 2022 2022-01 Thesis ID 18101551 ID 18101214 ID 18101093 ID 18101611 ID 18101673 http://hdl.handle.net/10361/16565 en 37 pages Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 37 pages application/pdf Brac University
institution Brac University
collection Institutional Repository
language English
topic RNN
CNN
melspectrogram
Audio feature extraction
Neural networks (Computer science)
spellingShingle RNN
CNN
melspectrogram
Audio feature extraction
Neural networks (Computer science)
Habib, Md.Adnan
Arefeen, Zarif Raiyan
Hussain, Arafat
Shahriyer, S.M.Rownak
Islam, Tanzid
Sound classification using deep learning for hard of hearing and deaf people
description This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
author2 Parvez, Mohammad Zavid
author_facet Parvez, Mohammad Zavid
Habib, Md.Adnan
Arefeen, Zarif Raiyan
Hussain, Arafat
Shahriyer, S.M.Rownak
Islam, Tanzid
format Thesis
author Habib, Md.Adnan
Arefeen, Zarif Raiyan
Hussain, Arafat
Shahriyer, S.M.Rownak
Islam, Tanzid
author_sort Habib, Md.Adnan
title Sound classification using deep learning for hard of hearing and deaf people
title_short Sound classification using deep learning for hard of hearing and deaf people
title_full Sound classification using deep learning for hard of hearing and deaf people
title_fullStr Sound classification using deep learning for hard of hearing and deaf people
title_full_unstemmed Sound classification using deep learning for hard of hearing and deaf people
title_sort sound classification using deep learning for hard of hearing and deaf people
publisher Brac University
publishDate 2022
url http://hdl.handle.net/10361/16565
work_keys_str_mv AT habibmdadnan soundclassificationusingdeeplearningforhardofhearinganddeafpeople
AT arefeenzarifraiyan soundclassificationusingdeeplearningforhardofhearinganddeafpeople
AT hussainarafat soundclassificationusingdeeplearningforhardofhearinganddeafpeople
AT shahriyersmrownak soundclassificationusingdeeplearningforhardofhearinganddeafpeople
AT islamtanzid soundclassificationusingdeeplearningforhardofhearinganddeafpeople
_version_ 1814308239677849600

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