Audio Tag Annotation and Retrieval Using Tag Count Information

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.