To facilitate information retrieval of large-scale music databases,
the detection of musical concepts, or auto-tagging,
has been an active research topic. This paper concerns the
use of concept correlations to improve musical concept detection.
We propose to formulate concept detection as an
ordinal regression problem to explicitly take advantage of
the ordinal relationship between concepts and avoid the
data imbalance problem of conventional multi-label classification
methods. To further improve the detection accuracy,
we propose to leverage the co-occurrence patterns
of concepts for context fusion and employ concept selection
to remove irrelevant or noisy concepts. Evaluation on
the cal500 dataset shows that we are able to improve the
detection accuracy of 174 concepts from 0.2513 to 0.2924.