Electrical sounds: preprocessed music for cochlear implant users
| dc.contributor.advisor | Gucluturk, Y. | |
| dc.contributor.advisor | Guclu, U. | |
| dc.contributor.author | Grootjen, Lizzy | |
| dc.date.issued | 2021-01-27 | |
| dc.description.abstract | Music perception for cochlear implant users is limited due to inaccurate pitch perception. As music is embedded in the cognitive system of humans, it is important to improve music perception for cochlear implant users. This study investigates how a neural network can improve pitch perception by amplifying the resonant frequencies in music. In the end, all frequencies were ampli ed instead of only the resonant frequencies, due to time constraints. The neural network started with a loss of 120,000 and ended with a loss of 1000, which indicates it learned features from the spectrogram of the music snippets. The online questionnaire did not show a signi cant result for the comparison between the original vocoded music and optimised vocoded music. There was a high variability between participants. The model can be improved by only amplifying the resonant frequencies, either implicitly or explicitly. The model can also be combined with other preprocessing strategies, such as F0mod and semitone mapping. | |
| dc.identifier.uri | https://theses.ubn.ru.nl/handle/123456789/15697 | |
| dc.language.iso | en | |
| dc.thesis.faculty | Faculteit der Sociale Wetenschappen | |
| dc.thesis.specialisation | specialisations::Faculteit der Sociale Wetenschappen::Artificial Intelligence::Bachelor Artificial Intelligence | |
| dc.thesis.studyprogramme | studyprogrammes::Faculteit der Sociale Wetenschappen::Artificial Intelligence | |
| dc.thesis.type | Bachelor | |
| dc.title | Electrical sounds: preprocessed music for cochlear implant users |
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