Estimating the Structural Segmentation of Popular Music Pieces Under Regularity Constraints
Music structure estimation has recently emerged as a central topic within the field of music information retrieval. Indeed, as music is a highly structured information stream, knowledge of how a music piece is organized represents a key challenge to enhance the management and exploitation of large music collections. This paper focuses on the benefits that can be expected from a regularity constraint on the structural segmentation of popular music pieces. Specifically, here, we study how a constraint that favors structural segments of comparable size provides a better conditioning of the boundary estimation process. First, we propose a formulation of the structural segmentation task as an optimization process, which separates the contribution from the audio features and the one from the constraint. We illustrate how the corresponding cost function can be minimized using a Viterbi algorithm. We present briefly its implementation and results in three systems designed for and submitted to the MIREX 2010, 2011, and 2012 evaluation campaigns. Then, we explore the benefits of the regularity constraint as an efficient mean for combining the outputs of a selection of systems presented at MIREX between 2010 and 2015, yielding a level of performance competitive to that of the state-of-the-art on the “MIREX10” dataset (100 J-Pop songs from the RWC database).