Abstract
In this paper, we describe and discuss the evaluation process and results of a content-based music retrieval system that we have developed. In our system, user models embody the ability of evolving and using different music similarity measures for different users. Specifically, a user-supplied relevance feedback and related neural network-based incremental learning procedures allows our system to determine which subset of a set of objective acoustic features approximates more efficiently the subjective music similarity perception of an individual user. The evaluation results verify our hypothesis of a direct relation between subjective music similarity perception and objective acoustic feature subsets. Moreover, it is shown that, after training, retrieved music pieces exhibit significantly improved perceived similarity to user-targeted music pieces.
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Sotiropoulos, D.N., Lampropoulos, A.S., Tsihrintzis, G.A. (2007). Evaluation of Modeling Music Similarity Perception Via Feature Subset Selection. In: Conati, C., McCoy, K., Paliouras, G. (eds) User Modeling 2007. UM 2007. Lecture Notes in Computer Science(), vol 4511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73078-1_32
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DOI: https://doi.org/10.1007/978-3-540-73078-1_32
Publisher Name: Springer, Berlin, Heidelberg
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