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Emotion in the singing voice—a deeper look at acoustic features in the light of automatic classification
Stockholm University, Faculty of Humanities, Department of Linguistics, Phonetics. KTH (Royal Institute of Technology), Sweden.ORCID iD: 0000-0002-1495-7773
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Number of Authors: 5
2015 (English)In: EURASIP Journal on Audio, Speech, and Music Processing, ISSN 1687-4714, E-ISSN 1687-4722, 19Article in journal (Refereed) Published
Abstract [en]

We investigate the automatic recognition of emotions in the singing voice and study the worth and role of a variety of relevant acoustic parameters. The data set contains phrases and vocalises sung by eight renowned professional opera singers in ten different emotions and a neutral state. The states are mapped to ternary arousal and valence labels. We propose a small set of relevant acoustic features basing on our previous findings on the same data and compare it with a large-scale state-of-the-art feature set for paralinguistics recognition, the baseline feature set of the Interspeech 2013 Computational Paralinguistics ChallengE (ComParE). A feature importance analysis with respect to classification accuracy and correlation of features with the targets is provided in the paper. Results show that the classification performance with both feature sets is similar for arousal, while the ComParE set is superior for valence. Intra singer feature ranking criteria further improve the classification accuracy in a leave-one-singer-out cross validation significantly.

Place, publisher, year, edition, pages
2015. 19
Keyword [en]
Emotion recognition, Singing voice, Acoustic features, Feature selection
National Category
Language Technology (Computational Linguistics) Musicology
Identifiers
URN: urn:nbn:se:su:diva-121185DOI: 10.1186/s13636-015-0057-6ISI: 000360837600001OAI: oai:DiVA.org:su-121185DiVA: diva2:857661
Available from: 2015-09-29 Created: 2015-09-28 Last updated: 2017-11-08Bibliographically approved

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Salomão, Gláucia LaísSundberg, JohanSchuller, Björn W.
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