Learning, Probability and Logic: Toward a Unified Approach for Content-Based Music Information Retrieval

Crayencour, Helene-Camille; Cella, Carmine-Emanuele

doi:10.3389/fdigh.2019.00006

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Logic Meets Learning1

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2020

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“…A possible candidate for such a framework is statistical relational artificial intelligence (StarAI), where learning and logical rules coexist. I discussed this idea, together with another researcher in MIR, in a recent perspective paper that focuses on the potential that StarAI has for modelling complex musical problems (Crayencour and Cella, 2019).…”

Section: Logic Meets Learningmentioning

confidence: 99%

Music Information Retrieval and Contemporary Classical Music: A Successful Failure

Cella

2020

Transactions of the International Society for Music Information Retrieval

Self Cite

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This paper is about the story of my relationship, as a contemporary music composer, with computational tools that are situated in the areas of signal processing, machine learning and music information retrieval (MIR). I believe that sharing this story can be useful to the MIR community since it illustrates the problems that can arise when you try to use these techniques in the context of contemporary music creation. Since this is a personal story, I will refer to experiences that I had during about fifteen years of usage of MIR-related technologies. I will show how these technologies tried to (unsuccessfully) shape my musical thinking and why I believe that some of them have come to an end. Finally, I will propose new possible directions for the future of MIR.

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Section: Logic Meets Learningmentioning

confidence: 99%