Music genre neuroimaging dataset

Tomoya Nakai; Naoko Koide-Majima; Shinji Nishimoto

doi:10.1016/j.dib.2021.107675

Music genre neuroimaging dataset

Data Brief. 2021 Dec 4:40:107675. doi: 10.1016/j.dib.2021.107675. eCollection 2022 Feb.

Authors

Tomoya Nakai^{1

2

3}, Naoko Koide-Majima^{1

4}, Shinji Nishimoto^{1

4

5}

Affiliations

¹ Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Japan.
² Lyon Neuroscience Research Center (CRNL), INSERM U1028 - CNRS UMR5292, University of Lyon, Lyon, France.
³ JSPS Overseas Research Fellow, Tokyo, Japan.
⁴ Graduate School of Frontier Biosciences, Osaka University, Suita, Japan.
⁵ Graduate School of Medicine, Osaka University, Suita, Japan.

Abstract

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.

Keywords: Brain activity; Machine learning; Music genre recognition; fMRI.