@inproceedings{d320d9449cdf422b882fe94984b93e51,
title = "From Sound to Map: Predicting Geographic Origin in Traditional Music Works",
abstract = "Music is a ubiquitous phenomenon. In today{\textquoteright}s world, no one can imagine life without its presence, and no one questions its significance in human life. This is not a new phenomenon but has been prevalent for hundreds of years. Therefore, an automated approach to understanding music plays a nontrivial role in science. One of the many tasks in Music Information Retrieval is the categorization of musical compositions. In this paper, the authors address the rarely explored topic of classifying traditional musical compositions from different cultures into regions (continents), subregions, and countries. A newly created dataset is presented, along with preliminary classification results using well-known classifiers. The presented work marks the beginning of a long and fascinating scientific journey.",
keywords = "Classification, Machine Learning, Music Information Retrieval, Traditional Music",
author = "Daniel Kostrzewa and Pawe{\l} Grabczy{\'n}ski",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 24th International Conference on Computational Science, ICCS 2024 ; Conference date: 02-07-2024 Through 04-07-2024",
year = "2024",
doi = "10.1007/978-3-031-63751-3\_12",
language = "English",
isbn = "9783031637537",
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 = "174--188",
editor = "Leonardo Franco and \{de Mulatier\}, Cl{\'e}lia and Maciej Paszynski and Krzhizhanovskaya, \{Valeria V.\} and Dongarra, \{Jack J.\} and Sloot, \{Peter M. A.\}",
booktitle = "Computational Science {\textendash} ICCS 2024 - 24th International Conference, Proceedings",
address = "Germany",
}