Huq, AminulRahman, RafeedHossain, Md. SakibIslam, Syed TamzidulMazumder, SujatJoy, Ali ImranSakib, Md. Sadman2023-08-202023-08-2020232023-03ID 18101201ID 22241133ID 18101300ID 18301179ID 18301061http://hdl.handle.net/10361/19457Cataloged from PDF version of thesis.Includes bibliographical references (pages 37-38).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.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.49 pagesenBrac 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.Sound classificationSpectrogramsSpeech commandCNNResNet50Neural networks (Computer science)Thesis