Pandey, PankajPankajPandeySharma, GulshanGulshanSharmaMiyapuram, Krishna P.Krishna P.MiyapuramSubramanian, RamanathanRamanathanSubramanianLomas, DerekDerekLomas2025-08-312025-08-312022-01-01[9781665405409]10.1109/ICASSP43922.2022.97473322-s2.0-85131242200http://repository.iitgn.ac.in/handle/IITG2025/26325Naturalistic 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.falsemusic perception | Neural signatures | repetitive musical patterns | song identificationConference Paper1246-1250202215cpConference Proceeding7