Abstract
Music has an extraordinary ability to evoke emotions. Nowadays, the music fruition mechanism is evolving, focusing on the music content. In this work, a novel approach for agglomerating songs on the basis of their emotional contents, is introduced. The main emotional features are extracted after a pre-processing phase where both Sparse Modeling and Independent Component Analysis based methodologies are applied. The approach makes it possible to summarize the main sub-tracks of an acoustic music song (e.g., information compression and filtering) and to extract the main features from these parts (e.g., music instrumental features). Experiments are presented to validate the proposed approach on collections of real songs.
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Acknowledgements
The research was entirely developed when Mario Iannicelli was a Bachelor Degree student in Computer Science at University of Naples Parthenope. The authors would like to thank Marco Gianfico for his support and comments. This work was partially funded by the University of Naples Parthenope (Sostegno alla ricerca individuale per il triennio 2016–2018 project).
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Iannicelli, M., Nardone, D., Ciaramella, A., Staiano, A. (2019). Content-Based Music Agglomeration by Sparse Modeling and Convolved Independent Component Analysis. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Quantifying and Processing Biomedical and Behavioral Signals. WIRN 2017 2017. Smart Innovation, Systems and Technologies, vol 103. Springer, Cham. https://doi.org/10.1007/978-3-319-95095-2_8
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DOI: https://doi.org/10.1007/978-3-319-95095-2_8
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