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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Sotiropoulos, D. N., Lampropoulos, A. S., & Tsihrintzis, G. A. (2008). Artificial immune system-based music genre classification. Studies in Computational Intelligence, 142, 191–200. https://doi.org/10.1007/978-3-540-68127-4_20
Mendeley helps you to discover research relevant for your work.