Vocal segment estimation in music pieces based on collaborative use of EEG and audio features

This paper presents a novel estimation method of segments including vocals in music pieces based on collaborative use of features extracted from electroencephalogram (EEG) signals recorded while users are listening to music pieces and features extracted from these audio signals. From extracted EEG features and audio features, we estimate segments including vocals based on Support Vector Machine (SVM) by separately utilizing these two features. Furthermore, the final classification results are obtained by integrating these estimation results based on supervised learning from multiple experts. Therefore, our method realizes multimodal estimation of segments including vocals in music pieces. Experimental results show the improvement of our method over the methods utilizing only EEG or audio features.