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Does the beat go on?: identifying rhythms from brain waves recorded after their auditory presentation

Published: 01 October 2014 Publication History

Abstract

Music imagery information retrieval (MIIR) systems may one day be able to recognize a song just as we think of it. As one step towards such technology, we investigate whether rhythms can be identified from an electroencephalography (EEG) recording taken directly after their auditory presentation. The EEG data has been collected during a rhythm perception study in Kigali, Rwanda and comprises 12 East African and 12 Western rhythmic stimuli presented to 13 participants. Each stimulus was presented as a loop for 32 seconds followed by a break of four seconds before the next one started. Using convolutional neural networks (CNNs), we are able to recognize individual rhythms with a mean accuracy of 22.9% over all subjects by just looking at the EEG recorded during the silence between the stimuli.

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  • (2020)Autoencoders in Deep Neural Network Architecture for Real Work ApplicationsHandbook of Research on Recent Developments in Electrical and Mechanical Engineering10.4018/978-1-7998-0117-7.ch007(214-236)Online publication date: 2020

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cover image ACM Other conferences
AM '14: Proceedings of the 9th Audio Mostly: A Conference on Interaction With Sound
October 2014
219 pages
ISBN:9781450330329
DOI:10.1145/2636879
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 01 October 2014

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Author Tags

  1. convolutional neural networks
  2. deep learning
  3. electroencephalography
  4. music information retrieval

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  • Research-article

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AM '14
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  • DOF
AM '14: Audio Mostly 2014
October 1 - 3, 2014
Aalborg, Denmark

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AM '14 Paper Acceptance Rate 29 of 49 submissions, 59%;
Overall Acceptance Rate 177 of 275 submissions, 64%

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View all
  • (2020)Autoencoders in Deep Neural Network Architecture for Real Work ApplicationsHandbook of Research on Recent Developments in Electrical and Mechanical Engineering10.4018/978-1-7998-0117-7.ch007(214-236)Online publication date: 2020

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