Music emotion recognition typically attempts to map audio features from music to a mood representation using machine learning techniques. In addition to having a good dataset, the key to a successful system is choosing the right inputs and outputs. Often, the inputs are based on a set of audio features extracted from a single software library, which may not be the most suitable combination. This paper describes how 47 different types of audio features were evaluated using a five-dimensional support vector regressor, trained and tested on production music, in order to find the combination which produces the best performance. The results show the minimum number of features that yield optimum performance, and which combinations are strongest for mood prediction.

Authors: Baume, Chris; Fazekas, György; Barthet, Mathieu; Marston, David; Sandler, Mark
Affiliations: BBC R&D, London, UK; Queen Mary University of London, London, UK(See document for exact affiliation information.)
AES Conference: 53rd International Conference: Semantic Audio (January 2014)
Paper Number: P1-3
Publication Date: January 27, 2014 Import into BibTeX
Subject: Machine Learning Methods for Audio Content Analysis
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=17110

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