Cost-sensitive stacking for audio tag annotation and retrieval

Audio tags correspond to keywords that people use to de scribe different aspects of a music clip, such as the genre, mood, and instrumentation. Since social tags are usually as signed by people with different levels of musical knowledge, they inevitably contain noisy information. By treating the tag counts as costs, we can model the audio tagging problem as a cost-sensitive classification problem. In addition, tag correlation is another useful information for automatic audio tagging since some tags often co-occur. By considering the co-occurrences of tags, we can model the audio tagging problem as a multi-label classification problem. To exploit the tag count and correlation information jointly, we formulate the audio tagging task as a novel cost-sensitive multi-label (CSML) learning problem. The results of audio tag annotation and retrieval experiments demonstrate that the new approach outperforms our MIREX 2009 winning method.