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2017 | OriginalPaper | Buchkapitel

Structured Prediction of Music Mood with Twin Gaussian Processes

verfasst von : Santosh Chapaneri, Deepak Jayaswal

Erschienen in: Pattern Recognition and Machine Intelligence

Verlag: Springer International Publishing

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Abstract

Music mood is one of the most frequently used descriptors when people search for music, but due to its subjective nature, it is difficult to accurately estimate mood. In this work, we propose a structured prediction framework to model the valence and arousal dimensions of mood jointly without requiring multiple regressors. A confidence-interval based estimated consensus from crowdsourced annotations is first learned along with reliabilities of various annotators to serve as the ground truth and is shown to perform better than using the average annotation values. A variational Bayesian approach is used to learn the Gaussian mixture model representation for acoustic features. Using an efficient implementation of Twin Gaussian process for structured regression, the proposed work achieves an improvement in \(R^2\) of \(9.3\%\) for arousal and \(18.2\%\) for valence relative to state-of-the-art techniques.

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Titel: Structured Prediction of Music Mood with Twin Gaussian Processes
verfasst von: Santosh Chapaneri
Deepak Jayaswal
Verlag: Springer International Publishing
Buch: Pattern Recognition and Machine Intelligence
Print ISBN: 978-3-319-69899-1

Electronic ISBN: 978-3-319-69900-4

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-69900-4_82

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