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This paper proposes the use of feature weights to reveal
the hierarchical nature of music audio. Feature weighting has been exploited
in machine learning, but has not been applied to music audio segmentation. We
describe both a global and a local approach to automatic feature weighting.
The global approach assigns a single weighting to all features in a song. The
local approach uses the local separability directly. Both approaches reveal structure
that is obscured by standard features, and emphasize segments of a particular
size. |
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