Diethe, T., Durrant, Simon, Shawe-Taylor, J. and Neubauer, H. (2009) Detection of changes in patterns of brain activity according to musical tonality. In: Artificial Intelligence and Applications, 17 - 18 February 2009, Innsbruck, Austria.
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Detection_of_changes_in_patterns_of_brain_activity_according_to_musical_tonality_(Diethe_2009).pdf - Whole Document Restricted to Repository staff only 148kB |
Item Type: | Conference or Workshop contribution (Paper) |
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Item Status: | Live Archive |
Abstract
A common structural element of Western tonal music is
the change of key within a melodic sequence. The present
paper examines data from a set of experiments that were
conducted to analyse human perception of different modulations of key. EEG recordings were taken of participants who were given melodic sequences containing changes in key of varying distances, as well as atonal sequences, with a behavioural task of identifying the change in key. Analysis of EEG involved derivation of 122120 separate dependent variables (features), including measures such as interelectrode spectral power, coherence, and phase. The paper presents a novel method of performing semantic dimension reduction that produces a representation enabling high accuracy identification of out-of-subject tonal verses atonal sequences.
Additional Information: | A common structural element of Western tonal music is the change of key within a melodic sequence. The present paper examines data from a set of experiments that were conducted to analyse human perception of different modulations of key. EEG recordings were taken of participants who were given melodic sequences containing changes in key of varying distances, as well as atonal sequences, with a behavioural task of identifying the change in key. Analysis of EEG involved derivation of 122120 separate dependent variables (features), including measures such as interelectrode spectral power, coherence, and phase. The paper presents a novel method of performing semantic dimension reduction that produces a representation enabling high accuracy identification of out-of-subject tonal verses atonal sequences. |
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Keywords: | Machine Learning, KCCA, EEG, Music, Tonality |
Subjects: | C Biological Sciences > C800 Psychology C Biological Sciences > C850 Cognitive Psychology C Biological Sciences > C860 Neuropsychology |
Divisions: | College of Social Science > School of Psychology |
ID Code: | 4725 |
Deposited On: | 12 Oct 2011 07:41 |
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