Abstract
Automatic deriving of similarity relations between music pieces is an inherent field of music information retrieval research. Due to the nearly unrestricted amount of musical data, the real-world similarity search algorithms have to be highly efficient and scalable. The possible solution is to represent each music excerpt with a statistical model (ex. Gaussian mixture model) and thus to reduce the computational costs by applying the parametric distance measures between the models. In this paper we discuss the combinations of applying different parametric modelling techniques and distance measures and weigh the benefits of each one against the others.
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Acknowledgements
This work has been partly supported by the PHAROS and the DIVAS projects, funded under the EC IST 6th Framework Program. Furthermore, the work on this publication is supported by grant No. 01QM07017 of the German THESEUS program.
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Lukashevich, H., Dittmar, C., Bastuck, C. (2009). Applying Statistical Models and Parametric Distance Measures for Music Similarity Search. In: Fink, A., Lausen, B., Seidel, W., Ultsch, A. (eds) Advances in Data Analysis, Data Handling and Business Intelligence. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01044-6_37
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DOI: https://doi.org/10.1007/978-3-642-01044-6_37
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