Music genre neuroimaging dataset

This dataset includes functional magnetic resonance imaging (fMRI) data collected while five subjects extensively listened to 540 music pieces from 10 music genres over the course of 3 days. Behavioral data are also available. Data are separated into training and test samples to facilitate the appli...

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Bibliographic Details
Published inData in brief Vol. 40; p. 107675
Main Authors Nakai, Tomoya, Koide-Majima, Naoko, Nishimoto, Shinji
Format Journal Article
LanguageEnglish
Published Netherlands Elsevier Inc 01.02.2022
Elsevier
Subjects
acoustics
brain
Brain activity
Data
data collection
fMRI
Machine learning
magnetism
music
Music genre recognition
neurophysiology
prediction
signal-to-noise ratio
fMRI
Music genre recognition
Brain activity
Machine learning
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Summary:This dataset includes functional magnetic resonance imaging (fMRI) data collected while five subjects extensively listened to 540 music pieces from 10 music genres over the course of 3 days. Behavioral data are also available. Data are separated into training and test samples to facilitate the application of machine learning algorithms. Test stimuli were repeated four times and can be used to evaluate the signal to noise ratio of brain activity. Using this dataset, both neuroimaging and machine learning researchers can test multiple algorithms regarding the prediction performance of brain activity induced by various music stimuli. Although two previous studies have used this dataset, there remains much room to apply different acoustic models. This dataset can contribute to integration of the fields of auditory neuroscience and machine learning.
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ISSN:2352-3409
2352-3409
DOI:10.1016/j.dib.2021.107675