Supervised machine learning for audio emotion recognition: Enhancing film sound design using audio features, regression models and artificial neural networks
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Wrexham Glyndwr University; Manchester Metropolitan University; London South Bank University; University of ChesterPublication Date
2020-04-22
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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.Citation
Cunningham, S., Ridley, H., Weinel, J., & Picking, R. (2020). Supervised machine learning for audio emotion recognition. Personal and Ubiquitous Computing, 25, 637–650. https://doi.org/10.1007/s00779-020-01389-0Publisher
SpringerAdditional Links
https://link.springer.com/article/10.1007/s00779-020-01389-0Type
ArticleDescription
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s00779-020-01389-0ISSN
1617-4909EISSN
1617-4917ae974a485f413a2113503eed53cd6c53
10.1007/s00779-020-01389-0
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Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0/