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MUSIC IDENTIFICATION USING BRAIN RESPONSES TO INITIAL SNIPPETS

Citation Author(s):
Pankaj Pandey, Gulshan Sharma, Krishna. P. Miyapuram, Ramanathan Subramaniam, Derek Lomas
Submitted by:
Pankaj Pandey
Last updated:
5 May 2022 - 12:17am
Document Type:
Presentation Slides
Document Year:
2022
Event:
Presenters:
Pankaj Pandey
Paper Code:
9011
Categories:
 

Naturalistic music typically contains repetitive musical patterns that are present throughout the song. These patterns form a signature, enabling effortless song recognition. We investigate whether neural responses corresponding to these repetitive patterns also serve as a signature, enabling recognition of later song segments on learning initial segments. We examine EEG encoding of naturalistic musical patterns employing the NMED-T and MUSIN-G datasets. Experiments reveal that (a) training machine learning classifiers on the initial 20s song segment enables accurate prediction of the song from the remaining segments; (b) β and γ band power spectra achieve optimal song classification, and (c) listener-specific EEG responses are observed for the same stimulus, characterizing individual differences in music perception.

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