Parvez, Mohammad ZavidRahman, RafeedHabib, Md.AdnanArefeen, Zarif RaiyanHussain, ArafatShahriyer, S.M.RownakIslam, Tanzid2022-04-252022-04-2520222022-01ID 18101551ID 18101214ID 18101093ID 18101611ID 18101673http://hdl.handle.net/10361/16565Cataloged from PDF version of thesis.Includes bibliographical references (pages 35-37).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.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.37 pagesen37 pagesBrac 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.RNNCNNmelspectrogramAudio feature extractionNeural networks (Computer science)Thesis