Supervised machine learning for audio emotion recognition

The field of Music Emotion Recognition has become and established research sub-domain of Music Information Retrieval. Less attention has been directed towards the counterpart domain of Audio Emotion Recognition, which focuses upon detection of emotional stimuli resulting from non-musical sound. By b...

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Veröffentlicht in:Personal and ubiquitous computing Jg. 25; H. 4; S. 637 - 650
Hauptverfasser: Cunningham, Stuart, Ridley, Harrison, Weinel, Jonathan, Picking, Richard
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer Nature B.V 01.08.2021
Schlagworte:
Acoustics
Arousal
Artificial neural networks
Correlation coefficients
Emotion recognition
Emotional factors
Emotions
Feature extraction
Information retrieval
Machine learning
Modelling
Multiple regression models
Neural networks
Sound
ISSN:1617-4909, 1617-4917
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Zusammenfassung:The field of Music Emotion Recognition has become and established research sub-domain of Music Information Retrieval. Less attention has been directed towards the counterpart domain of Audio Emotion Recognition, which focuses upon detection of emotional stimuli resulting from non-musical sound. By better understanding how sounds provoke emotional responses in an audience, it may be possible to enhance the work of sound designers. The work in this paper uses the International Affective Digital Sounds set. A total of 76 features are extracted from the sounds, spanning the time and frequency domains. The features are then subjected to an initial analysis to determine what level of similarity exists between pairs of features measured using Pearson’s r correlation coefficient before being used as inputs to a multiple regression model to determine their weighting and relative importance. The features are then used as the input to two machine learning approaches: regression modelling and artificial neural networks in order to determine their ability to predict the emotional dimensions of arousal and valence. It was found that a small number of strong correlations exist between the features and that a greater number of features contribute significantly to the predictive power of emotional valence, rather than arousal. Shallow neural networks perform significantly better than a range of regression models and the best performing networks were able to account for 64.4% of the variance in prediction of arousal and 65.4% in the case of valence. These findings are a major improvement over those encountered in the literature. Several extensions of this research are discussed, including work related to improving data sets as well as the modelling processes.
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ISSN:1617-4909
1617-4917
DOI:10.1007/s00779-020-01389-0