@inproceedings{53e8889c316745e88c7db116be609a41,
title = "Wide Ensembles of Neural Networks in Music Genre Classification",
abstract = "The classification of music genres is essential due to millions of songs in online databases. It would be nearly impossible or very costly to do this job manually. That is why there is a need to create robust and efficient methods that automatically help to do this task. In this paper, music genre recognition is implemented by exploiting the potential of wide ensembles of neural network classifiers. Creating infrequently used types of ensembles is a main contribution of authors in the development of automatic recognition of the musical genre. The paper shows how it can be done in a relatively quick and straightforward manner. The presented method can be implemented in many other use cases.",
keywords = "Classification, Free music archive dataset, Machine learning, Music genre recognition, Neural network, Wide ensemble",
author = "Daniel Kostrzewa and Wojciech Mazur and Robert Brzeski",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 22nd Annual International Conference on Computational Science, ICCS 2022 ; Conference date: 21-06-2022 Through 23-06-2022",
year = "2022",
doi = "10.1007/978-3-031-08754-7\_9",
language = "English",
isbn = "9783031087530",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "64--71",
editor = "Derek Groen and \{de Mulatier\}, Cl{\'e}lia and Krzhizhanovskaya, \{Valeria V.\} and Sloot, \{Peter M.A.\} and Maciej Paszynski and Dongarra, \{Jack J.\}",
booktitle = "Computational Science - ICCS 2022, 22nd International Conference, Proceedings",
address = "Germany",
}