A Comparative Study on Music Genre Classification Algorithms

被引：0
作者：
Stokowiec, Wojciech [1 ]
机构：
[1] Natl Informat Proc Inst, Warsaw, Poland
来源：
MACHINE INTELLIGENCE AND BIG DATA IN INDUSTRY | 2016年 / 19卷
关键词：
Music genre recognition; Million Song Dataset; Machine learning;
D O I：
10.1007/978-3-319-30315-4_11
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Music Genre Classification is one of the fundamental tasks in the field of Music Information Retrieval (MIR). In this paper the performance of various music genre classification algorithms including Random Forests, Multi-class Support Vector Machines and Deep Belief Networks is being compared. The study is based on the "Million Song Dataset" a freely-available collection of audio features and metadata. The emphasis is put not only on classification accuracy but also on robustness and scalability of algorithms.
引用
收藏
页码：123 / 132
页数：10
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