Abstract
We present a novel approach for the problem of automated music genre classification, which utilizes an Artificial Immune System (AIS)-based classifier. Our inspiration lies in the observation that the natural immune system has the intrinsic property of self/non-self cell discrimination, especially when the non-self (complementary) space of cells is significantly larger than the class of self cells. The AIS-based classifier that we have built is compared with KNN-, RBF- and SVM-based classifiers in various experiments involving music data. We find that the performance of our classifier is similar to that of the other classifiers when tested in multi-class (eg. four class) problems. On the other hand, it exceeds by a significant margin the performance of the other classifiers when tested in two class problems.
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References
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Sotiropoulos, D.N., Lampropoulos, A.S., Tsihrintzis, G.A. (2008). Artificial Immune System-Based Music Genre Classification. In: Tsihrintzis, G.A., Virvou, M., Howlett, R.J., Jain, L.C. (eds) New Directions in Intelligent Interactive Multimedia. Studies in Computational Intelligence, vol 142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68127-4_20
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DOI: https://doi.org/10.1007/978-3-540-68127-4_20
Publisher Name: Springer, Berlin, Heidelberg
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