@inproceedings{d89743a1f0544b1383f416b3880b99b6,
title = "Tempo and Time Signature Detection of a Musical Piece",
abstract = "Tempo and time signature detection are essential tasks in the field of Music Information Retrieval. These features often affect the perception of a piece of music. Their automatic estimation unlocks many possibilities for further audio processing, as well as supporting music recommendation systems and automatic song tagging. In this article, the main focus is on building a two-phase system for extracting both features. The influence of many parameters of known methods was investigated. The results were also compared with the well-known and scientifically recognized Librosa library for music processing.",
keywords = "Audio Features, GTZAN, Music Information Retrieval, Sound Analysis, Tempo, Time Signature",
author = "Daniel Kostrzewa and Marek Zabialowicz",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 23rd International Conference on Computational Science, ICCS 2023 ; Conference date: 03-07-2023 Through 05-07-2023",
year = "2023",
doi = "10.1007/978-3-031-35995-8\_48",
language = "English",
isbn = "9783031359941",
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 = "683--695",
editor = "Ji{\v r}{\'i} Miky{\v s}ka 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 2023 - 23rd International Conference, Proceedings",
address = "Germany",
}