| dc.contributor.author | Sathiamoorthy, Sharmilendran | |
| dc.date.accessioned | 2025-06-27T07:10:57Z | |
| dc.date.available | 2025-06-27T07:10:57Z | |
| dc.date.issued | 2024 | |
| dc.identifier.citation | Sathiamoorthy, Sharmilendran (2024) Environment Mapping With Sound Localization and Classification for the Hearing Impaired. BSc. Dissertation, Informatics Institute of Technology | en_US |
| dc.identifier.issn | 2019926 | |
| dc.identifier.uri | http://dlib.iit.ac.lk/xmlui/handle/123456789/2733 | |
| dc.description.abstract | "Sound and music are a right of humankind, it should be heard, felt and deeply absorbed, the capability to deliver the ability for everyone to understand and to loose themselves into the realms of music, irrespective of their disabilities, or forms, in this current frame of time is only possible through the strong arm of computer science. This research and its contents are aimed to pioneer a new domain where human perception of sound and AI are weaved together. This paper examines the role of sound source classification and localization. We examine the current state of sound classification using artificial intelligence (AI), paying particular attention to methods for estimating sound source distances. Through a comprehensive literature review, we identify key challenges and opportunities in this area. We then present the rationale for developing an environmental detection algorithm based on acoustic segmentation. This system has the potential to significantly improve the mobility and independence of people with hearing loss by providing real-time environmental information the paper identifies the specific research question and existing knowledge gaps that this project aims to address. We describe in detail the research design that has been implemented and the way we are addressing the identified challenges. Finally, we present a high-level project timeline with key deliverables for each phase. This research has the potential to contribute significantly to the build of applications that can improve the quality of life for people with hearing loss." | en_US |
| dc.language.iso | en | en_US |
| dc.subject | AudioClassification | en_US |
| dc.subject | Transfer learning | en_US |
| dc.subject | Proximity estimation | en_US |
| dc.title | Environment Mapping With Sound Localization and Classification for the Hearing Impaired | en_US |
| dc.type | Thesis | en_US |