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This Provisional PDF

EURASIP JournSpeech, and M

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Music clasmappings

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1 Introduction

Retail and online m

people often need

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music representatio

specific music class

the classes (e.g., gen

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protocols for each da

tations [34,35], the m

music representation

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denoted by R+.

A variety of norms o

counting the numbe

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in [44]. The audio s

44.9 Hz to 11 KHz

each element of the

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the whole pitch rang

The chroma as well

d ib d i b

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features (variables),

pr common latent va

Si th di i

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Problem (4) is solve

equivalent to

i

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M(r)[t+1]

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Algorithm 1 Solvin

Input: Training da

parameters r, r = Output: The set of

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on the high-dimensi

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electronic, folk, hip

world. In this datase

Th U i (h //

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That is, 78 tags, wh

dataset, were used in

F l [3] i di d

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5.2 Experimental

In Tables 1, 2, and 3

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Table 3 Music tagg

Method

LRSMs

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Table 5 Music clas

music recordings

Method

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measured figures of

deterioration. It is w

reported when audio

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chroma features in t

music classification

highlights the potent

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(Utrecht, 913 A

5. N Scaringella, G

Signal Proc. Ma

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22. Y Panagakis, C

auditory tempor

Tech. 18(3),576

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41. ML Zhang, ZH

40(7),2038204

42. Y Panagakis, C

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60. K Aryafar, A Sh

1st ACM Int. Wo

(Scottsdale, 28 N

61. C Osendorfer, J

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Figure 1