Encoding and Decoding of Music-Genre Representations in the Human Brain. Nakai, T., Koide-Majima, N., & Nishimoto, S. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 584–589, October, 2018. ISSN: 2577-1655doi abstract bibtex Music-genre recognition (MGR) has been a central issue in understanding human preferences of music. Previous studies have used various acoustic features to achieve MGR, though it has been largely unknown how music genres and related features are represented in the brain. Here, we measured brain activity while subjects passively listened to naturalistic music of various genres. A voxel-wise encoding model showed different activation patterns for each music genre in the bilateral superior temporal gyrus. We further performed music-genre classification using both a feature-based approach and a brain activity-based approach. Both approaches provided above-chance classification accuracy. Among four feature models, a biologically plausible spectro-temporal modulation transfer function (MTF) model showed the highest performance. These results provide a new insight into biologically plausible models of music genre.
@inproceedings{nakai_encoding_2018,
title = {Encoding and {Decoding} of {Music}-{Genre} {Representations} in the {Human} {Brain}},
doi = {10.1109/SMC.2018.00108},
abstract = {Music-genre recognition (MGR) has been a central issue in understanding human preferences of music. Previous studies have used various acoustic features to achieve MGR, though it has been largely unknown how music genres and related features are represented in the brain. Here, we measured brain activity while subjects passively listened to naturalistic music of various genres. A voxel-wise encoding model showed different activation patterns for each music genre in the bilateral superior temporal gyrus. We further performed music-genre classification using both a feature-based approach and a brain activity-based approach. Both approaches provided above-chance classification accuracy. Among four feature models, a biologically plausible spectro-temporal modulation transfer function (MTF) model showed the highest performance. These results provide a new insight into biologically plausible models of music genre.},
booktitle = {2018 {IEEE} {International} {Conference} on {Systems}, {Man}, and {Cybernetics} ({SMC})},
author = {Nakai, Tomoya and Koide-Majima, Naoko and Nishimoto, Shinji},
month = oct,
year = {2018},
note = {ISSN: 2577-1655},
keywords = {Biological system modeling, Brain modeling, Decoding, Encoding, Feature extraction, MRI, MTF, Music, Training, decoding, music genre},
pages = {584--589},
}
Downloads: 0
{"_id":"XWp8pLcStEiDxXgAQ","bibbaseid":"nakai-koidemajima-nishimoto-encodinganddecodingofmusicgenrerepresentationsinthehumanbrain-2018","author_short":["Nakai, T.","Koide-Majima, N.","Nishimoto, S."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Encoding and Decoding of Music-Genre Representations in the Human Brain","doi":"10.1109/SMC.2018.00108","abstract":"Music-genre recognition (MGR) has been a central issue in understanding human preferences of music. Previous studies have used various acoustic features to achieve MGR, though it has been largely unknown how music genres and related features are represented in the brain. Here, we measured brain activity while subjects passively listened to naturalistic music of various genres. A voxel-wise encoding model showed different activation patterns for each music genre in the bilateral superior temporal gyrus. We further performed music-genre classification using both a feature-based approach and a brain activity-based approach. Both approaches provided above-chance classification accuracy. Among four feature models, a biologically plausible spectro-temporal modulation transfer function (MTF) model showed the highest performance. These results provide a new insight into biologically plausible models of music genre.","booktitle":"2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","author":[{"propositions":[],"lastnames":["Nakai"],"firstnames":["Tomoya"],"suffixes":[]},{"propositions":[],"lastnames":["Koide-Majima"],"firstnames":["Naoko"],"suffixes":[]},{"propositions":[],"lastnames":["Nishimoto"],"firstnames":["Shinji"],"suffixes":[]}],"month":"October","year":"2018","note":"ISSN: 2577-1655","keywords":"Biological system modeling, Brain modeling, Decoding, Encoding, Feature extraction, MRI, MTF, Music, Training, decoding, music genre","pages":"584–589","bibtex":"@inproceedings{nakai_encoding_2018,\n\ttitle = {Encoding and {Decoding} of {Music}-{Genre} {Representations} in the {Human} {Brain}},\n\tdoi = {10.1109/SMC.2018.00108},\n\tabstract = {Music-genre recognition (MGR) has been a central issue in understanding human preferences of music. Previous studies have used various acoustic features to achieve MGR, though it has been largely unknown how music genres and related features are represented in the brain. Here, we measured brain activity while subjects passively listened to naturalistic music of various genres. A voxel-wise encoding model showed different activation patterns for each music genre in the bilateral superior temporal gyrus. We further performed music-genre classification using both a feature-based approach and a brain activity-based approach. Both approaches provided above-chance classification accuracy. Among four feature models, a biologically plausible spectro-temporal modulation transfer function (MTF) model showed the highest performance. These results provide a new insight into biologically plausible models of music genre.},\n\tbooktitle = {2018 {IEEE} {International} {Conference} on {Systems}, {Man}, and {Cybernetics} ({SMC})},\n\tauthor = {Nakai, Tomoya and Koide-Majima, Naoko and Nishimoto, Shinji},\n\tmonth = oct,\n\tyear = {2018},\n\tnote = {ISSN: 2577-1655},\n\tkeywords = {Biological system modeling, Brain modeling, Decoding, Encoding, Feature extraction, MRI, MTF, Music, Training, decoding, music genre},\n\tpages = {584--589},\n}\n\n","author_short":["Nakai, T.","Koide-Majima, N.","Nishimoto, S."],"key":"nakai_encoding_2018","id":"nakai_encoding_2018","bibbaseid":"nakai-koidemajima-nishimoto-encodinganddecodingofmusicgenrerepresentationsinthehumanbrain-2018","role":"author","urls":{},"keyword":["Biological system modeling","Brain modeling","Decoding","Encoding","Feature extraction","MRI","MTF","Music","Training","decoding","music genre"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://api.zotero.org/groups/4476011/items?key=BfP7bN7FF9dJwtyiLBORewdg&format=bibtex&limit=100","dataSources":["Xr58FaQSpr3DSFeK8"],"keywords":["biological system modeling","brain modeling","decoding","encoding","feature extraction","mri","mtf","music","training","decoding","music genre"],"search_terms":["encoding","decoding","music","genre","representations","human","brain","nakai","koide-majima","nishimoto"],"title":"Encoding and Decoding of Music-Genre Representations in the Human Brain","year":2018}