Human-centered favorite music estimation: EEG-based extraction of audio features reflecting individual preference

Abstract

This paper presents a human-centered method for favorite music estimation using EEG-based audio features. In order to estimate user's favorite musical pieces, our method utilizes his/her EEG signals for calculating new audio features suitable for representing the user's music preference. Specifically, projection, which transforms original audio features into the features reflecting the preference, is calculated by applying kernel Canonical Correlation Analysis (CCA) to the audio features and the EEG features which are extracted from the user's EEG signals during listening to favorite musical pieces. By using the obtained projection, the new EEG-based audio features can be derived since this projection provides the best correlation between the user's EEG signals and their corresponding audio signals. Thus, successful estimation of user's favorite musical pieces via a Support Vector Machine (SVM) classifier using the new audio features becomes feasible. Since our method does not need acquisition of EEG signals for obtaining new audio features from new musical pieces after calculating the projection, this indicates the high practicability of our method. Experimental results show that our method outperforms methods using original audio features or EEG features.