From Interactive to Adaptive Mood-Based Music Listening Experiences in Social or Personal Contexts

Abstract

Listeners of audio are increasingly shifting to a participatory culture where technology allows them to modify and control the listening experience. This report describes the developments of a mood-driven music player, Moodplay, which incorporates semantic computing technologies for musical mood using social tags and informative and aesthetic browsing visualizations. The prototype runs with a dataset of over 10,000 songs covering various genres, arousal, and valence levels. Changes in the design of the system were made in response to user evaluations from over 120 participants in 15 different sectors of work or education. The proposed client/server architecture integrates modular components powered by semantic web technologies and audio content feature extraction. This enables recorded music content to be controlled in flexible and nonlinear ways. Dynamic music objects can be used to create mashups on the fly of two or more simultaneous songs to allow selection of multiple moods. The authors also consider nonlinear audio techniques that could transform the player into a creative tool, for instance, by reorganizing, compressing, or expanding temporally prerecorded content.