Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine learning for music genre: multifaceted review and
  experimentation with audioset   Music genre classification is one of the sub-disciplines of music information
retrieval (MIR) with growing popularity among researchers, mainly due to the
already open challenges. Although research has been prolific in terms of number
of published works, the topic still suffers from a problem in its foundations:
there is no clear and formal definition of what genre is. Music categorizations
are vague and unclear, suffering from human subjectivity and lack of agreement.
In its first part, this paper offers a survey trying to cover the many
different aspects of the matter. Its main goal is give the reader an overview
of the history and the current state-of-the-art, exploring techniques and
datasets used to the date, as well as identifying current challenges, such as
this ambiguity of genre definitions or the introduction of human-centric
approaches. The paper pays special attention to new trends in machine learning
applied to the music annotation problem. Finally, we also include a music genre
classification experiment that compares different machine learning models using
Audioset.

2
The categories of this paper are: math
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio