Combining Content-Based Auto-Taggers with Decision-Fusion

To automatically annotate songs with descriptive keywords, a variety of content-based auto-tagging strategies have been proposed in recent years. Different approaches may capture different aspects of a song’s musical content, such as timbre, temporal dynamics, rhythmic qualities, etc. As a result, some auto-taggers may be better suited to model the acoustic characteristics commonly associated with one set of tags, while being less predictive for other tags. This paper proposes decision-fusion, a principled approach to combining the predictions of a diverse collection of content-based autotaggers that focus on various aspects of the musical signal. By modeling the correlations between tag predictions of different auto-taggers, decision-fusion leverages the benefits of each of the original auto-taggers, and achieves superior annotation and retrieval performance.