Title :
Convex non-negative matrix factorization for automatic music structure identification
Author :
Nieto, Oriol ; Jehan, Tristan
Author_Institution :
Music & Audio Res. Lab., New York Univ., New York, NY, USA
Abstract :
We propose a novel and fast approach to discover structure in western popular music by using a specific type of matrix factorization that adds a convex constrain to obtain a decomposition that can be interpreted as a set of weighted cluster centroids. We show that these centroids capture the different sections of a musical piece (e.g. verse, chorus) in a more consistent and efficient way than classic non-negative matrix factorization. This technique is capable of identifying the boundaries of the sections and then grouping them into different clusters. Additionally, we evaluate this method on two different datasets and show that it is competitive compared to other music segmentation techniques, outperforming other matrix factorization methods.
Keywords :
audio signal processing; matrix decomposition; music; Western popular music; automatic music structure identification; convex constrain; convex nonnegative matrix factorization; music segmentation technique; musical piece; weighted cluster centroids; Clustering algorithms; Conferences; Feature extraction; Matrix decomposition; Music information retrieval; Sparse matrices; Vectors; matrix factorization; music structure analysis; segmentation;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
Conference_Location :
Vancouver, BC
DOI :
10.1109/ICASSP.2013.6637644