Authors:
Eren Atsiz
;
Erinç Albey
and
Enis Kayış
Affiliation:
Industrial Engineering, Özyeğin University, Istanbul and Turkey
Keyword(s):
Music Information Retrieval, Genre Classification.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Business Analytics
;
Business Intelligence
;
Data Analytics
;
Data Engineering
;
Information Retrieval
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Pattern Recognition
;
Software Engineering
;
Symbolic Systems
Abstract:
Musical genres are labels created by human and based on mutual characteristics of songs, which are also called musical features. These features are key indicators for the content of the music. Rather than predictions by human decisions, developing an automatic solution for genre classification has been a significant issue over the last decade. In order to have automatic classification for songs, different approaches have been indicated by studying various datasets and part of songs. In this paper, we suggest an alternative genre classification method based on which part of songs have to be used to have a better accuracy level. Wide range of acoustic features are obtained at the end of the analysis and discussed whether using full versions or pieces of songs is better. Both alternatives are implemented and results are compared. The best accuracy level is 55% while considering the full version of songs. Besides, additional analysis for Turkish songs is also performed. All analysis, dat
a, and results are visualized by a dynamic dashboard system, which is created specifically for the study.
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