Knees, P., Schedl, M., & Goto, M. (2020). Intelligent User Interfaces for Music Discovery. Transactions of the International Society for Music Information Retrieval, 3(1), 165–179. https://doi.org/10.5334/tismir.60
Transactions of the International Society for Music Information Retrieval
Number of Pages:
Ubiquity Press Ltd.
recommender systems; user interfaces; music browsing; music access; content-based MIR; community metadata
Assisting the user in finding music is one of the original motivations that led to the establishment of Music Information Retrieval (MIR) as a research field. This encompasses classic Information Retrieval inspired access to music repositories that aims at meeting an information need of an expert user. Beyond this, however, music as a cultural art form is also connected to an entertainment need of potential listeners, requiring more intuitive and engaging means for music discovery. A central aspect in this process is the user interface.
In this article, we reflect on the evolution of MIR-driven intelligent user interfaces for music browsing and discovery over the past two decades. We argue that three major developments have transformed and shaped user interfaces during this period, each connected to a phase of new listening practices. Phase 1 has seen the development of content-based music retrieval interfaces built upon audio processing and content description algorithms facilitating the automatic organization of repositories and finding music according to sound qualities. These interfaces are primarily connected to personal music collections or (still) small commercial catalogs. Phase 2 comprises interfaces incorporating collaborative and automatic semantic description of music, exploiting knowledge captured in user-generated metadata. These interfaces are connected to collective web platforms. Phase 3 is dominated by recommender systems built upon the collection of online music interaction traces on a large scale. These interfaces are connected to streaming services.
We review and contextualize work from all three phases and extrapolate current developments to outline possible scenarios of music recommendation and listening interfaces of the future.
Information Systems Engineering: 90% Visual Computing and Human-Centered Technology: 10%