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Some Thoughts on the How, What and Why of Music Informatics Research

Abstract: The framework of music informatics research (MIR) can be thought of as a closed loop of data collection, algorithmic development and benchmarking. Much of what we do is heavily focused on the algorithmic aspects, or how to optimally combine various techniques from e.g., signal processing, data mining, and machine learning, to solve a variety of problems, from auto-tagging to automatic transcription, that captivate the interest of our community. We are very good at this, and in this talk I will describe some of the know-how that we have collectively accumulated over the years. On the other hand, I would argue that we are less proficient at clearly defining the “what” and “why” behind our work, that data collection and benchmarking have received far less attention and are often treated as afterthoughts, and that we sometimes tend to rely on widespread and limiting assumptions about music that affect the validity and usability of our research. On this, there is much that we can learn from music theory and cognition research, particularly with regards to the adoption of methods and practices that fully embrace our prior knowledge about music, and the complexity and variability of human responses to it.

Juan Pablo Bello is Professor of Music Technology and Computer Science & Engineering at New York University.  Sponsored by the Department of Electrical and Computer Engineering

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