A Sound classification and display tool for assisting the deaf and hard-of-hearing: a case of Kenya

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dc.contributor.authorWanjiru, R. W.
dc.date.accessioned2023-10-03T09:58:43Z
dc.date.available2023-10-03T09:58:43Z
dc.date.issued2023
dc.descriptionFull- text thesis
dc.description.abstractSound is an essential component of existence in all aspects of life. It is a crucial component when it comes to creating automated systems for various domains such as personal safety and essential surveillance. Hearing people always absorb information through sound and language that is spoken around them. On the other hand, deaf and hard of hearing people lack the luxury of hearing and may end up having major problems due to lack of this awareness. Various researches have shown that there is a mismatch between the need and the demand of the assistive technologies as you find that the need is high but the demand and supply is low which impose a challenge in enhancing access of the assistive devices. Also, there is a gap between the number of people who require assistive technologies to meet their needs and the number of people who are willing and able to purchase and use these technologies. This mismatch could be due to factors such as the cost of the technologies, lack of awareness or knowledge about the technologies, or cultural barriers to their use. Only a small percentage of people have access to the assistive devices. This study reviewed the existing assistive technologies for the deaf and hard of hearing. Prior studies on assistive technologies for the deaf revealed that, sound classification systems have been developed world wide, but none have been implemented for use in Kenya. The research employed a machine learning approach, specifically utilizing convolutional neural networks, to design a sound classification model. The process involved transforming detected sound events into spectrogram images, which were then processed by the Convolutional Neural Network to extract relevant features. The extracted features were subsequently employed to classify environmental sounds, including car horns, dog barking among others. Once the sounds have been classified, a mobile app was used to display a notification indicating the type of sound that has been detected. The machine learning model was evaluated for its effectiveness in assisting the deaf and hard-of-hearing individuals, with the ability to accurately classify a wide range of urban sounds relevant to the study and display corresponding notifications on the user interface. The development of this model stems from a strong motivation to empower deaf individuals, enabling them to experience greater independence without relying on others, with an aim to bridge the gap between auditory awareness and the needs of the deaf and hard-of-hearing community. Keywords: Deaf and Hard of Hearing, Convolutional Neural Networks, Sound Classification, Spectrogram.
dc.identifier.citationWanjiru, R. W. (2023). A Sound classification and display tool for assisting the deaf and hard-of-hearing: A case of Kenya [Strathmore University]. http://hdl.handle.net/11071/13522
dc.identifier.urihttp://hdl.handle.net/11071/13522
dc.language.isoen
dc.publisherStrathmore University
dc.titleA Sound classification and display tool for assisting the deaf and hard-of-hearing: a case of Kenya
dc.typeThesis
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MSIT Theses and Dissertations (2023)