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International Symposium on Methodologies for Intelligent Systems

ISMIS 2020: Foundations of Intelligent Systems pp 150–160Cite as

Static Music Emotion Recognition Using Recurrent Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12117))

Abstract

The article presents experiments using recurrent neural networks for emotion detection for musical segments using Russell’s circumplex model. A process of feature extraction and creating sequential data for learning networks with long short-term memory (LSTM) units is presented. Models were implemented using the WekaDeeplearning4j package and a number of experiments were carried out with data with different sets of features and varying segmentation. The usefulness of dividing data into sequences as well as the sense of using recurrent networks to recognize emotions in music, whose results have even exceeded the SVM algorithm for regression, were demonstrated. The author analyzed the effect of the network structure and the set of used features on the results of regressors recognizing values on two axes of the emotion model: arousal and valence.

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Notes

  1. 1.

    http://marsyas.info/downloads/datasets.html.

  2. 2.

    http://essentia.upf.edu/documentation/algorithms_reference.html.

  3. 3.

    https://deeplearning4j.org.

References

  1. Aljanaki, A., Yang, Y.H., Soleymani, M.: Developing a benchmark for emotional analysis of music. PLoS One 12(3), e0173392 (2017)

    Article  Google Scholar 

  2. Bachorik, J., Bangert, M., Loui, P., Larke, K., Berger, J., Rowe, R., Schlaug, G.: Emotion in motion: investigating the time-course of emotional judgments of musical stimuli. Music Percept. 26, 355–364 (2009)

    Google Scholar 

  3. Bogdanov, D., Wack, N., Gómez, E., Gulati, S., Herrera, P., Mayor, O., Roma, G., Salamon, J., Zapata, J., Serra, X.: ESSENTIA: an audio analysis library for music information retrieval. In: Proceedings of the 14th International Society for Music Information Retrieval Conference, pp. 493–498 (2013)

    Google Scholar 

  4. Chowdhury, S., Portabella, A.V., Haunschmid, V., Widmer, G.: Towards explainable music emotion recognition: the route via mid-level features. In: Proceedings of the 20th International Society for Music Information Retrieval Conference, ISMIR 2019, Delft, The Netherlands, pp. 237–243 (2019)

    Google Scholar 

  5. Coutinho, E., Trigeorgis, G., Zafeiriou, S., Schuller, B.: Automatically estimating emotion in music with deep long-short term memory recurrent neural networks. In: Working Notes Proceedings of the MediaEval 2015 Workshop, Wurzen, Germany (2015)

    Google Scholar 

  6. Delbouys, R., Hennequin, R., Piccoli, F., Royo-Letelier, J., Moussallam, M.: Music mood detection based on audio and lyrics with deep neural net. In: Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR 2018), Paris, France, pp. 370–375 (2018)

    Google Scholar 

  7. Gers, F.A., Schmidhuber, J., Cummins, F.A.: Learning to forget: continual prediction with LSTM. Neural Comput. 12, 2451–2471 (2000)

    Article  Google Scholar 

  8. Grekow, J.: Audio features dedicated to the detection of four basic emotions. In: Saeed, K., Homenda, W. (eds.) CISIM 2015. LNCS, vol. 9339, pp. 583–591. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24369-6_49

    Chapter  Google Scholar 

  9. Grekow, J.: Music emotion maps in arousal-valence space. In: Saeed, K., Homenda, W. (eds.) CISIM 2016. LNCS, vol. 9842, pp. 697–706. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45378-1_60

    Chapter  Google Scholar 

  10. Grekow, J.: Human annotation. In: From Content-Based Music Emotion Recognition to Emotion Maps of Musical Pieces. SCI, vol. 747, pp. 13–24. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-70609-2

  11. Lang, S., Bravo-Marquez, F., Beckham, C., Hall, M., Frank, E.: WekaDeeplearning4j: a deep learning package for Weka based on Deeplearning4j. Knowl.-Based Syst. 178, 48–50 (2019)

    Article  Google Scholar 

  12. Lu, L., Liu, D., Zhang, H.J.: Automatic mood detection and tracking of music audio signals. Trans. Audio Speech Lang. Proc. 14(1), 5–18 (2006)

    Article  Google Scholar 

  13. Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980)

    Article  Google Scholar 

  14. Tzanetakis, G., Cook, P.: Marsyas: a framework for audio analysis. Org. Sound 4(3), 169–175 (2000)

    Article  Google Scholar 

  15. Weninger, F., Eyben, F., Schuller, B.: On-line continuous-time music mood regression with deep recurrent neural networks. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5412–5416 (2014)

    Google Scholar 

  16. Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques, 4th edn. Morgan Kaufmann Publishers Inc., San Francisco (2016)

    Google Scholar 

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Acknowledgments

This research was realized as part of study no. KSIiSK in the Bialystok University of Technology and financed with funds from the Ministry of Science and Higher Education.

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Authors and Affiliations

  1. Faculty of Computer Science, Bialystok University of Technology, Wiejska 45A, 15-351, Bialystok, Poland

    Jacek Grekow

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  1. Jacek Grekow
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Correspondence to Jacek Grekow .

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Editors and Affiliations

  1. Graz University of Technology, Graz, Austria

    Denis Helic

  2. University of Klagenfurt, Klagenfurt, Austria

    Gerhard Leitner

  3. Graz University of Technology, Graz, Austria

    Martin Stettinger

  4. Graz University of Technology, Graz, Austria

    Alexander Felfernig

  5. University of North Carolina at Charlotte, Charlotte, NC, USA

    Zbigniew W. Raś

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Grekow, J. (2020). Static Music Emotion Recognition Using Recurrent Neural Networks. In: Helic, D., Leitner, G., Stettinger, M., Felfernig, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2020. Lecture Notes in Computer Science(), vol 12117. Springer, Cham. https://doi.org/10.1007/978-3-030-59491-6_14

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  • DOI: https://doi.org/10.1007/978-3-030-59491-6_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59490-9

  • Online ISBN: 978-3-030-59491-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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