Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Music Classification: Beyond Supervised Learning, Towards Real-world
  Applications   Music classification is a music information retrieval (MIR) task to classify
music items to labels such as genre, mood, and instruments. It is also closely
related to other concepts such as music similarity and musical preference. In
this tutorial, we put our focus on two directions - the recent training schemes
beyond supervised learning and the successful application of music
classification models.
  The target audience for this web book is researchers and practitioners who
are interested in state-of-the-art music classification research and building
real-world applications. We assume the audience is familiar with the basic
machine learning concepts.
  In this book, we present three lectures as follows: 1. Music classification
overview: Task definition, applications, existing approaches, datasets, 2.
Beyond supervised learning: Semi- and self-supervised learning for music
classification, 3. Towards real-world applications: Less-discussed, yet
important research issues in practice.

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The categories of this paper are: gr-qc
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The categories of this paper are: cmp-lg
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The categories of this paper are: cond-mat
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The categories of this paper are: math-ph
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The categories of this paper are: stat
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The categories of this paper are: q-fin
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The categories of this paper are: solv-int
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The categories of this paper are: plasm-ph
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The categories of this paper are: adap-org
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The categories of this paper are: quant-ph
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The categories of this paper are: funct-an
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The categories of this paper are: hep-th
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The categories of this paper are: nlin
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The categories of this paper are: hep-lat
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The categories of this paper are: chao-dyn
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The categories of this paper are: physics