Learning to segment songs with ordinal linear discriminant analysis
Brian McFee, Daniel P. W. Ellis · 2014
This paper describes a supervised learning algorithm which optimizes a feature representation for temporally constrained clustering. The proposed method is applied to music segmentation, in which a song is partitioned into functional or locally homogeneous segments (e.g., verse or chorus). To facilitate abstraction over multiple training examples, we develop a latent structural repetition feature, which summarizes the repetitive structure of a song of any length in a fixed-dimensional representation. Experimental results demonstrate that the proposed method efficiently integrates heterogeneous features, and improves segmentation accuracy.