Time Variability-Based Hierarchic Recognition of Multiple Musical Instruments in Recordings, Chapter 18

Elżbieta Kubera , Alicja A. Wieczorkowska , Zbigniew W. Raś

Abstract

The research reported in this chapter is focused on automatic identification of musical instruments in polyphonic audio recordings. Random forests have been used as a classification tool, pre-trained as binary classifiers to indicate presence or absence of a target instrument. Feature set includes parameters describing frame-based properties of a sound. Moreover, in order to capture the patterns which emerge on the time scale, new temporal parameters are introduced to supply additional temporal information for the timbre recognition. In order to achieve higher estimation rate, we investigated a feature-driven hierarchical classification of musical instruments built using agglomerative clustering strategy. Experiments showed that the performance of classifiers based on this new classification of instruments schema is better than performance of the traditional flat classifiers, which directly estimate the instrument. Also, they outperform the classifiers based on the classical Hornbostel-Sachs schema.
Author Elżbieta Kubera - [University of Life Sciences in Lublin]
Elżbieta Kubera,,
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, Alicja A. Wieczorkowska - [Polsko-Japońska Akademia Technik Komputerowych]
Alicja A. Wieczorkowska,,
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- Polsko-Japońska Akademia Technik Komputerowych
, Zbigniew W. Raś (FEIT / IN)
Zbigniew W. Raś,,
- The Institute of Computer Science
Pages347-363
Book Skowron Andrzej, Suraj Zbigniew (eds.): Rough Sets and Intelligent Systems – Professor Zdzisław Pawlak in Memoriam. Volume 2, Intelligent Systems Reference Library, vol. 43, 2013, Heidelberg New York Dordrecht London, Springer-Verlag, ISBN 978-3-642-30340-1, 604 p., DOI:10.1007/978-3-642-30341-8
front-matter-RSIS.pdf / 344.73 KB / No licence information
Keywords in EnglishMusic information retrieval – automatic indexing – timbre recognition – pitch tracking – Hornbostel-Sachs system – temporal data mining – random forest – agglomerative clustering
ASJC Classification3309 Library and Information Sciences; 1802 Information Systems and Management; 1700 General Computer Science
DOIDOI:10.1007/978-3-642-30341-8_18
URL http://link.springer.com/chapter/10.1007/978-3-642-30341-8_18
Languageen angielski
Score (nominal)5
ScoreMinisterial score = 5.0, 08-01-2020, MonographChapterAuthor
Publication indicators Scopus Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2013 = 0.269
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