Speech command classification based on deep neural networks

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.

Dades bibliogràfiques
Autors principals:	Hossain, Md. Sakib, Islam, Syed Tamzidul, Mazumder, Sujat, Joy, Ali Imran, Sakib, Md. Sadman
Altres autors:	Huq, Aminul
Format:	Thesis
Idioma:	English
Publicat:	Brac University 2023
Matèries:	Sound classification Spectrograms Speech command CNN ResNet50 Neural networks (Computer science)
Accés en línia:	http://hdl.handle.net/10361/19457

id	10361-19457
record_format	dspace
spelling	10361-194572023-08-20T21:02:38Z Speech command classification based on deep neural networks Hossain, Md. Sakib Islam, Syed Tamzidul Mazumder, Sujat Joy, Ali Imran Sakib, Md. Sadman Huq, Aminul Rahman, Rafeed Department of Computer Science and Engineering, Brac University Sound classification Spectrograms Speech command CNN ResNet50 Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 37-38). In our day-to-day life there are lots of sounds that we are processing. To process these sounds our brain absorb sound signals and provide us informative knowledge. For human being this is not possible to extract every sounds properly so that, there are lots of equipment which helps us to extract essential information from an audio source. Around the year lots of model came to help thorough extract informations using various algorithms. Also, some models are Convolutional Neural Network (CNN), Region-Convolutional Neural Network (R-CNN), Artificial Neural Network (ANN), VGG16, ResNet50 and Numerous machine learning algorithms have been utilized to effectively categorize audio, and these methods have recently demonstrated encouraging results in separating spectrotemporal images from various sound classifications. The study purpose of this research was to analyze which feature extraction method shows maximum result using Convolutional Neural Network (CNN), VGG16 and ResNet50. In the proposed model, MFCC feature extraction method are taken from the dataset and trained using a multiple layer-based con volution neural network. In the experimental assessment, a sound dataset consisting of 105829 audio clips separated up into multiple groups of important sounds during study used to develop the models. Additionally, we evaluated the models’ validity which reach an accuracy of 94.53% on Speech Command dataset. Md. Sakib Hossain Syed Tamzidul Islam Sujat Mazumder Ali Imran Joy Md. Sadman Sakib B. Computer Science 2023-08-20T06:02:54Z 2023-08-20T06:02:54Z 2023 2023-03 Thesis ID 18101201 ID 22241133 ID 18101300 ID 18301179 ID 18301061 http://hdl.handle.net/10361/19457 en 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. 49 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Sound classification Spectrograms Speech command CNN ResNet50 Neural networks (Computer science)
spellingShingle	Sound classification Spectrograms Speech command CNN ResNet50 Neural networks (Computer science) Hossain, Md. Sakib Islam, Syed Tamzidul Mazumder, Sujat Joy, Ali Imran Sakib, Md. Sadman Speech command classification based on deep neural networks
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
author2	Huq, Aminul
author_facet	Huq, Aminul Hossain, Md. Sakib Islam, Syed Tamzidul Mazumder, Sujat Joy, Ali Imran Sakib, Md. Sadman
format	Thesis
author	Hossain, Md. Sakib Islam, Syed Tamzidul Mazumder, Sujat Joy, Ali Imran Sakib, Md. Sadman
author_sort	Hossain, Md. Sakib
title	Speech command classification based on deep neural networks
title_short	Speech command classification based on deep neural networks
title_full	Speech command classification based on deep neural networks
title_fullStr	Speech command classification based on deep neural networks
title_full_unstemmed	Speech command classification based on deep neural networks
title_sort	speech command classification based on deep neural networks
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/19457
work_keys_str_mv	AT hossainmdsakib speechcommandclassificationbasedondeepneuralnetworks AT islamsyedtamzidul speechcommandclassificationbasedondeepneuralnetworks AT mazumdersujat speechcommandclassificationbasedondeepneuralnetworks AT joyaliimran speechcommandclassificationbasedondeepneuralnetworks AT sakibmdsadman speechcommandclassificationbasedondeepneuralnetworks
_version_	1814308011020124160

Speech command classification based on deep neural networks

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