TY - GEN
T1 - Evolvable hybrid ensembles for musical genre classification
AU - Kostrzewa, Daniel
AU - Ciszynski, Michal
AU - Brzeski, Robert
N1 - Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/7/9
Y1 - 2022/7/9
N2 - The possibility of classifying musical songs according to their musical genre is becoming more challenging because of the millions of songs included in online databases. Therefore, reliable and efficient methods need to be developed that will automatically solve this task. In this article, the mentioned task is accomplished using sets of classifiers. The authors' contribution to the development of automatic musical genre recognition is using hybrid ensembles formed from deep neural networks and classical classifiers and the optimization process executed on the voting process of individual classifiers. Finally, differential evolution algorithms have been used to improve the classification quality further. The proposed evolutionary algorithm shows improvement in comparison with other optimization methods.
AB - The possibility of classifying musical songs according to their musical genre is becoming more challenging because of the millions of songs included in online databases. Therefore, reliable and efficient methods need to be developed that will automatically solve this task. In this article, the mentioned task is accomplished using sets of classifiers. The authors' contribution to the development of automatic musical genre recognition is using hybrid ensembles formed from deep neural networks and classical classifiers and the optimization process executed on the voting process of individual classifiers. Finally, differential evolution algorithms have been used to improve the classification quality further. The proposed evolutionary algorithm shows improvement in comparison with other optimization methods.
KW - classical classifiers
KW - deep neural networks
KW - ensembles
KW - evolution algorithm
KW - music information retrieval
KW - optimization
UR - https://www.scopus.com/pages/publications/85136329025
U2 - 10.1145/3520304.3528792
DO - 10.1145/3520304.3528792
M3 - Conference contribution
AN - SCOPUS:85136329025
T3 - GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference
SP - 252
EP - 255
BT - GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference
PB - Association for Computing Machinery, Inc
T2 - 2022 Genetic and Evolutionary Computation Conference Companion , GECCO 2022
Y2 - 9 July 2022 through 13 July 2022
ER -