Audio tag annotation and retrieval using tag count information
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Journal Volume
6523 LNCS
Journal Issue
PART 1
Pages
339-349
Date Issued
2011
Author(s)
Abstract
Audio tags correspond to keywords that people use to describe different aspects of a music clip, such as the genre, mood, and instrumentation. With the explosive growth of digital music available on the Web, automatic audio tagging, which can be used to annotate unknown music or retrieve desirable music, is becoming increasingly important. This can be achieved by training a binary classifier for each tag based on the labeled music data. However, since social tags are usually assigned by people with different levels of musical knowledge, they inevitably contain noisy information. To address the noisy label problem, we propose a novel method that exploits the tag count information. By treating the tag counts as costs, we model the audio tagging problem as a cost-sensitive classification problem. The results of audio tag annotation and retrieval experiments show that the proposed approach outperforms our previous method, which won the MIREX 2009 audio tagging competition. © 2011 Springer-Verlag Berlin Heidelberg.
Subjects
Audio tag annotation; audio tag retrieval; cost-sensitive evaluation; cost-sensitive learning; folksonomy; tag count
SDGs
Other Subjects
Audio tag annotation; audio tag retrieval; cost-sensitive evaluation; cost-sensitive learning; Folksonomies; tag count; Audio tag annotation; Audio tag retrieval; Cost-sensitive evaluation; Cost-sensitive learning; Folksonomies; Tag count; Classification (of information); Costs; Semantic Web; Artificial intelligence; Computer science; Computers; Audio acoustics; Audio acoustics
Type
conference paper