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Towards music imagery information retrieval: Introducing the OpenMIIR dataset of EEG recordings from music perception and imagination
Authors
Jessica A. Grahn
Adrian M. Owen
Avital Sternin
Sebastian Stober
Publication date
1 January 2015
Publisher
Scholarship@Western
Abstract
© Sebastian Stober, Avital Sternin, Adrian M. Owen and Jessica A. Grahn. Music imagery information retrieval (MIIR) systems may one day be able to recognize a song from only our thoughts. As a step towards such technology, we are presenting a public domain dataset of electroencephalography (EEG) recordings taken during music perception and imagination. We acquired this data during an ongoing study that so far comprises 10 subjects listening to and imagining 12 short music fragments – each 7–16s long – taken from well-known pieces. These stimuli were selected from different genres and systematically vary along musical dimensions such as meter, tempo and the presence of lyrics. This way, various retrieval scenarios can be addressed and the success of classifying based on specific dimensions can be tested. The dataset is aimed to enable music information retrieval researchers interested in these new MIIR challenges to easily test and adapt their existing approaches for music analysis like fingerprinting, beat tracking, or tempo estimation on EEG data
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Last time updated on 23/11/2020