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Evaluation of Audio Feature Groups for the Prediction of Arousal and Valence in Music

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Abstract

Computer-aided prediction of arousal and valence ratings helps to automatically associate emotions with music pieces, providing new music categorisation and recommendation approaches, and also theoretical analysis of listening habits. The impact of several groups of music properties like timbre, harmony, melody or rhythm on perceived emotions has often been studied in literature. However, only little work has been done to extensively measure the potential of specific feature groups, when they supplement combinations of other possible features already integrated into the regression model. In our experiment, we measure the performance of multiple linear regression applied to combinations of energy, harmony, rhythm and timbre audio features to predict arousal and valence ratings. Each group is represented by a smaller number of dimensions estimated with the help of Minimum Redundancy–Maximum Relevance (MRMR) feature selection. The results show that cepstral timbre features are particularly useful to predict arousal, and rhythm features are the most relevant to predict valence.

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Acknowledgements

We thank Philipp Kramer for providing the code and explanations of experiments from his bachelor’s thesis, in particular for the extraction of MFCC and OBSC features.

Author information

Authors and Affiliations

  1. Department of Computer Science, Technische Universität Dortmund, Dortmund, Germany

    Igor Vatolkin

  2. Institute of Communication Acoustics, Ruhr-Universität Bochum, Bochum, Germany

    Anil Nagathil

Authors
  1. Igor Vatolkin
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  2. Anil Nagathil
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Corresponding author

Correspondence to Igor Vatolkin .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Dortmund University of Applied Sciences and Arts, Dortmund, Germany

    Nadja Bauer

  2. Faculty of Statistics, TU Dortmund University, Dortmund, Germany

    Katja Ickstadt

  3. Institute for Empirical Research and Statistics, FOM University of Applied Sciences, Essen, Germany

    Karsten Lübke

  4. School of Business Studies, HOST University of Applied Sciences Stralsund, Stralsund, Germany

    Gero Szepannek

  5. Department of Information Systems, University of Münster, Münster, Germany

    Heike Trautmann

  6. Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy

    Maurizio Vichi

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Vatolkin, I., Nagathil, A. (2019). Evaluation of Audio Feature Groups for the Prediction of Arousal and Valence in Music. In: Bauer, N., Ickstadt, K., Lübke, K., Szepannek, G., Trautmann, H., Vichi, M. (eds) Applications in Statistical Computing. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-030-25147-5_19

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