Human-Centered Favorite Music Classification Using EEG-Based Individual Music Preference Via Deep Time-Series CCA

A method to classify a user's like or dislike musical pieces based on the extraction of his or her music preference is proposed in this paper. New scheme of Canonical Correlation Analysis (CCA), called Deep Time-series CCA (DTCCA), which can consider the correlation between two sets of input fe...

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Bibliographic Details
Main Authors: Sawata, Ryosuke, Ogawa, Takahiro, Haseyama, Miki
Format: Conference Proceeding
Language:English
Subjects:
canonical correlation analysis (CCA)
Conferences
Correlation
deep neural network (DNN)
electroencephalogram (EEG)
Electroencephalography
Feature extraction
Music
music classification
Neural networks
recurrent neural network (RNN)
Signal processing
time-series data
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Summary:A method to classify a user's like or dislike musical pieces based on the extraction of his or her music preference is proposed in this paper. New scheme of Canonical Correlation Analysis (CCA), called Deep Time-series CCA (DTCCA), which can consider the correlation between two sets of input features with considering the timeseries relation lurked in each input data is exploited to realize the aforementioned classification. One of the most difference between DTCCA and existing other CCAs is enabling to consider the above time-series relation, and thus DTCCA make the individual electroencephalogram (EEG)-based favorite music classification more effective than the methods using one of other CCAs instead of DTCCA since EEG and audio signals are respectively time-series data. Experimental results show that DTCCA-based favorite music classification outperformed not only method using original features without CCA but also methods using other existing CCAs including even state-of-the-art CCA.
ISSN:2379-190X
DOI:10.1109/ICASSP39728.2021.9414945