Sadeque, Farig YousufArony, Radia TasnimJalil, Naomi AfrinChowdhury, Maisha RaidahTahsin, Umma HafsaHasan, Mohammad Shabab Bin2026-01-082026-01-0820252025-10ID 21201209ID 21201325ID 21201560ID 21201523ID 21201285http://hdl.handle.net/10361/27412Cataloged from PDF version of thesis.Includes bibliographical references (pages 53-55).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2025.A significant portion of the global population suffers to some extent from hearing loss and requires assistive devices. The everyday lives of hearing-impaired individuals are hampered by their inability to recognize important environmental sounds in their surroundings. This thesis proposes an innovative system that can both recognize and localize critical environmental sounds while providing tactile feedback to enhance user awareness in real time. Machine learning models such as Convolutional Neural Networks (CNNs) are utilized to classify specific sounds such as car horn, dog bark, scream, and calling bell. Furthermore, localization algorithms are integrated into a wearable device to determine the direction of these sounds. Once these sounds are detected and localized, the user is notified through vibration feedback from their smartphone or smartwatch, while the sound type, confidence level, and direction are simultaneously displayed. The proposed system is designed to be a low-cost, compact, and accessible solution for recognizing important environmental sounds and represents a meaningful advancement in the field of assistive devices by significantly enhancing the safety and quality of life of individuals with hearing impairments.62 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.Wearable technologyHearing impairmentAudio classificationRealtime feedbackSound localizationTactile feedbackMachine learningConvolutional neural networksMobile applicationsWear OSAndroid developmentAccessibility technologyHuman-computer interactionOn-device inferenceEnvirnomental sound detectionSelf-help devices for people with disabilities.Assistive computer technology.Deaf people--Rehabilitation.Hearing aids--Technological innovations.Directional hearing.Signal processing--Digital techniques.Auditory perception.Neural networks (Computer science).Thesis