Beveridge, Scott

Knox, Don

English

Emotion classification algorithms rely on an extraction process to create numerical representations of raw music information. The result is a feature vector which is used to characterise the audio signal within a specific context or application. Frequency domain analysis is the primary method of creating these features which range from low-level acoustical parameters to high-level structural representations. Previous studies in music emotion classification have concentrated on feature sets for classical, film and folk music with little attention being given to western contemporary music. This may be due to the constraints of the composition and production techniques associated with this type of content which create difficulties in extracting meaningful features. Despite these issues the ubiquity of this genre shows a clear need for work in this area. Furthermore, if any emotion classification system is to have `real-world' value it must have the ability to deal with this type of popular content.</p

ResearchOnline@GCU

Emotion classification of Western contemporary music: identifying a representativefeature setBeveridge, Scott; Knox, DonPublication date:2010Link to publication in ResearchOnlineCitation for published version (Harvard):Beveridge, S & Knox, D 2010, 'Emotion classification of Western contemporary music: identifying arepresentative feature set'. <http://researchonline.gcu.ac.uk/sec/85>General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.Take down policyIf you believe that this document breaches copyright please view our takedown policy at https://edshare.gcu.ac.uk/id/eprint/5179 for detailsof how to contact us.Download date: 29. Apr. 2020Glasgow Caledonian UniversityResearch Online @ GCUSchool of Engineering & Computing1-8-2010Emotion classification of Western contemporarymusic: identifying a representative feature setScott BeveridgeGlasgow Caledonian University, Scott.Beveridge@gcu.ac.ukDon KnoxGlasgow Caledonian University, d.knox@gcu.ac.ukPaper presented at the 11th International Conference on Music Perception and Cognition, Seattle,Washington, 23-27 August 2010. Conference website: http://www.icad.org/node/3102This Conference Paper is brought to you for free and open access by Research Online @ GCU. It has been accepted for inclusion in the repository byan authorized administrator of Research Online @ GCU. For more information, please contact repository@gcu.ac.uk.Digital Commons CitationBeveridge, Scott and Knox, Don, "Emotion classification of Western contemporary music: identifying a representative feature set"(2010). School of Engineering & Computing. Paper 85.http://researchonline.gcu.ac.uk/sec/85Emotion classification of Western contemporary Music: Identifyinga representative feature setBackground Emotion classification algorithms rely on an extraction pro-cess to create numerical representations of raw music information. The re-sult is a feature vector which is used to characterise the audio signal withina specific context or application. Frequency domain analysis is the primarymethod of creating these features which range from low-level acoustical pa-rameters to high-level structural representations. Previous studies in musicemotion classification have concentrated on feature sets for classical, filmand folk music with little attention being given to western contemporarymusic. This may be due to the constraints of the composition and produc-tion techniques associated with this type of content which create difficultiesin extracting meaningful features. Despite these issues the ubiquity of thisgenre shows a clear need for work in this area. Furthermore, if any emotionclassification system is to have ‘real-world’ value it must have the ability todeal with this type of popular content.Aims This paper describes the creation of a feature set which can be usedto identify the expressed emotional qualities of western contemporary music.By combining low-level acoustical features with high level structural aspectssuch as melodic complexity an optimum feature set was created which char-acterizes contemporary music in an emotion classification context.Method A corpus of novel music tracks was assessed by participants us-ing a time-continuous emotion measurement methodology. Based on the 2-dimensional circumplex model this approach provided participant responsesbased on factors of arousal and valence. The stimuli were selected by a panel1of expert listeners to represent a popular music style.Signal analysis techniques were used to extract an initial vector of 586 fea-tures: acoustical and psychoacoustical features were extracted using signalanalysis techniques, and a symbolic MIDI representation for structural char-acteristics. Through a series of statistical analyses a subset of features wasselected which are most highly correlated with participant responses.Results The corpus showed strong correlation with a number acousticaland structural features. Timbral features correlated highly with overall mea-sures of both valence and arousal. Tonal measures also showed a strongrelationship with arousal.Conclusions This study provides evidence of a relationship between a sub-set of musical features and expressed emotion in Western contemporary mu-sic. In particular it highlights the need to combine acoustical, psychoacous-tical and structural to establish an optimum feature set.Topic Areas Music and emotionsKey Words Contemporary music, music emotion classification, music in-formation retrieval.2

Emotion classification of Western contemporary music: identifying a representative feature set

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Emotion classification of Western contemporary music: identifying a representative feature set

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