Performances of Low-level Audio Classifiers for Large-scale Music Similarity

被引：0

作者：

Osmalskyj, Julien ^{[1
]}

Van Droogenbroeck, Marc ^{[1
]}

Embrechts, Jean-Jacques ^{[1
]}

机构：

[1] Univ Liege, Dept EECS, INTELSIG Lab, B-4000 Liege, Belgium

来源：

21ST INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING (IWSSIP 2014) | 2014年

关键词：

Music similarity; rejectors; audio features fusion; Million Song Dataset;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes a survey of the performances of binary classifiers based on low-level audio features, for music similarity in large-scale databases. Various low-level descriptors are used individually and then combined using several fusion schemes in a content-based audio retrieval system. We show the performances of the classifiers in terms of pruning and loss and we demonstrate that some combination schemes achieve a better performance at a minimum computational cost.

引用

页码：91 / 94

页数：4

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