This study proposes a method for the automatic design of personalized playlists that places a listener in a positive mood (uplifting or relaxing feelings) based on individual's impressions of audio tracks. This method designs a playlist with gradually changing personal impressions of audio tracks and induces individuals' moods to be uplifting or relaxing. It estimates the subjective impressions of all audio tracks in the music database using the bagging method, a type of ensemble learning, and creates a personalized playlist. This study investigated the changes in a listener's mood before and after listening to personalized playlists, non-personalized playlists, and playlists with randomly selected tracks using a psychological scale. Consequently, many personalized playlists have shifted the listener into a positive mood more than non-personalized playlists or random design playlists.

This paper proposes a method for the automatic design of personalized playlists that place a listener in a positive mood (uplifting or relaxing feelings) based on an individual's impressions and pleasurable feelings of music. This method designs a playlist with gradually changing personal impressions of music and evokes positive moods with pleasant feelings. It estimates the subjective impressions and pleasurable feelings of all music in the database using the bagging method, a type of ensemble learning, and creates personalized playlists. This study investigates the changes in a listener's mood before and after listening to playlists suitable for personal impressions, those for personal impressions and nonpersonalized pleasure, and those for personal impressions and pleasure, using a psychological scale. Consequently, numerous personalized playlists concerning impressions and pleasure have shifted the listener into a more positive mood than other types of playlists.