Kansei Song Selection System Based on Music Fluctuation Features and Comparison of Song Selection Algorithm

Abstract

Recently, we have so many songs due to a large capacity of storage, however it becomes difficult to select the song using bibliographic data. In this paper, we propose Kansei song selection system based on music fluctuation features which named YSSS. In this system, the acoustic fluctuation features that show time variations of music are extracted from each songs, and the Kansei evaluations of the song are obtained through the subjective evaluation experiments. Then the features and the evaluations are related to each other, and the estimation spaces for Kansei evaluations are constructed. Using this estimation spaces, the Kansei evaluations are labeled with the songs. We proposed two types of song selection method, Exactly-Matching and Tolerant-List which considers the tolerance of human instinct. Through the subjective evaluation experiment, we confirmed the correct song selection and the contentment with proposed system. Furthermore, we confirmed that proposed system with Tolerant-List method shows better usability.