Characterizing Musical Correlates of Large-Scale Discovery Behavior

Jun 15, 2019



The ever-increasing size and accessibility of vast music libraries has created a demand more than ever for artificial systems that are capable of understanding, organizing, or even generating such complex data. While this topic has received relatively marginal attention within the machine learning community, it has been an area of intense focus within the community of Music Information Retrieval (MIR). While significant progress has been made, these problems remain far from solved. Furthermore, the recommender systems community has made great advances in terms of collaborative feedback recommenders, but these approaches suffer strongly from the cold-start problem. As such, recommendation techniques often fall back on content-based machine learning systems, but defining musical similarity is extremely challenging as myriad features all play some role (e.g., cultural, emotional, timbral, rhythmic). Thus, for machines must actually understand music to achieve an expert level of music recommendation. On the other side of this problem sits the recent explosion of work in the area of machine creativity. Relevant examples are both Google Magenta and the startup Jukedeck, who seek to develop algorithms capable of composing and performing completely original (and compelling) works of music. These algorithms require a similar deep understanding of music and present challenging new problems for the machine learning and AI community at large. This workshop proposal is timely in that it will bridge these separate pockets of otherwise very related research. And in addition to making progress on the challenges above, we hope to engage the wide AI and machine learning community with our nebulous problem space, and connect them with the many available datasets the MIR community has to offer (e.g., Audio Set, AcousticBrainz, Million Song Dataset), which offer near commercial scale to the academic research community.



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