Conclusion and Future Work

Abstract

As of December 2018, Spotify, which only a few years would have been considered a small niche company, is valued by investors at over 22 billion US Dollars (https://seekingalpha.com/article/4229513-spotify-valuation-problem) and has over 80 million paying subscribers (https://www.statista.com/statistics/244995/number-of-paying-spotify-subscribers/). Robotic musicianship has evolved from isolated novelty to a growing field of research with audience-inspiring outcomes (Hoffman and Weinberg in Auton Robot 31(2–3):133–153, [1]). Between 2002 and 2017 the number of members in the International Society for Music Information Retrieval has grown from about 500 to well over 2000 (https://www.ismir.net/stats.php). As the field of artificial intelligence for music-related tasks continues to grow, so grows the need for better, more robust learning systems that are able to more deeply understand people’s responses, preferences, and intentions to music. Such learning systems are not only valuable in the specific contexts in which understanding music, and human psychology with respect to music, is valuable, but also has broader implications regarding learning systems in general. From reasoning about implicit mental states in human counterparts and how they are affected by surrounding stimuli, to temporal adaptation to changing preferences and circumstances, to online context-aware recommendation, music serves as an excellent testbed for a wider, broader array of more general questions involving continual learning, system adaptation, and social agents.