Uncovering Audio Patterns in Music with Nonnegative Tucker Decomposition for Structural Segmentation

Axel Marmoret (1) , Jérémy E Cohen (1) , Nancy Bertin (1) , Frédéric Bimbot (1)

1 PANAMA - Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio

Recent work has proposed the use of tensor decomposition to model repetitions and to separate tracks in loop-based electronic music. The present work investigates further on the ability of Nonnegative Tucker Decompositon (NTD) to uncover musical patterns and structure in pop songs in their audio form. Exploiting the fact that NTD tends to express the content of bars as linear combinations of a few patterns, we illustrate the ability of the decomposition to capture and single out repeated motifs in the corresponding compressed space, which can be interpreted from a musical viewpoint. The resulting features also turn out to be efficient for structural segmentation, leading to experimental results on the RWC Pop data set which are potentially challenging state-of-the-art approaches that rely on extensive example-based learning schemes.

Domaines: Traitement du signal et de l'image [eess.SP]; Informatique [cs]; Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]; Mathématiques [math]

Axel Marmoret, Jérémy E Cohen, Nancy Bertin, Frédéric Bimbot. Uncovering Audio Patterns in Music with Nonnegative Tucker Decomposition for Structural Segmentation. ISMIR 2020 - 21st International Society for Music Information Retrieval, Oct 2020, Montréal (Online), Canada. pp.1-7. ⟨hal-02928733⟩