Search
Abstract
The paper compares the results of audio excerpt assignment to a music genre obtained in listening tests and classification by means of decision algorithms. A short review on music description employing music styles and genres is given. Then, assumptions of listening tests to be carried out along with an online survey for assigning audio samples to selected music genres are presented. A framework for music parametrization is created resulting in feature vectors, which are checked for data redundancy. Finally, the effectiveness of the automatic music genre classification employing two decision algorithms is presented. Conclusions contain the results of the comparative analysis of the results obtained in listening tests and automatic genre classification.
Citations
-
0
CrossRef
-
0
Web of Science
-
0
Scopus
Authors (4)
Cite as
Full text
full text is not available in portal
Keywords
Details
- Category:
- Monographic publication
- Type:
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Title of issue:
- Intelligent Methods and Big Data in Industrial Applications strony 291 - 305
- Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- DOROCHOWICZ A., Majdańczuk A., Hoffmann P., Kostek B.: Classification of Music Genres by Means of Listening Tests and Decision Algorithms// Intelligent Methods and Big Data in Industrial Applications/ ed. Robert Bembenik, Łukasz Skonieczny, Grzegorz Protaziuk, Marzena Kryszkiewicz, Henryk Rybinski Cham: Springer, 2018, s.291-305
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-319-77604-0_21
- Verified by:
- Gdańsk University of Technology
seen 220 times