Nonnegative Tucker Decomposition with Beta-divergence for Music Structure Analysis of Audio Signals
Nonnegative Tucker decomposition (NTD), a tensor decomposition model, has received increased interest in the recent years because of its ability to blindly extract meaningful patterns, in particular in Music Information Retrieval. Nevertheless, existing algorithms to compute NTD are mostly designed for the Euclidean loss. This work proposes a multiplicative updates algorithm to compute NTD with the beta-divergence loss, often considered a better loss for audio processing. We notably show how to implement efficiently the multiplicative rules using tensor algebra. Finally, we show on a music structure analysis task that unsupervised NTD fitted with beta-divergence loss outperforms earlier results obtained with the Euclidean loss.
Code (2)
Tasks
Information RetrievalMusic Information RetrievalRetrievaltensor algebraTensor DecompositionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Uncovering audio patterns in music with Nonnegative Tucker Decomposition for structural segmentation
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…
Tensor DecompositionAutomatic Relevance Determination in Nonnegative Matrix Factorization with the β-Divergence
This paper addresses the estimation of the latent dimensionality in nonnegative matrix factorization (NMF) with the \beta-divergence. The \beta-divergence is a family of cost functions that includes the squared Euclidean…
Stock Price PredictionOrthogonal Nonnegative Tucker Decomposition
In this paper, we study the nonnegative tensor data and propose an orthogonal nonnegative Tucker decomposition (ONTD). We discuss some properties of ONTD and develop a convex relaxation algorithm of the augmented Lagrang…
Face RecognitionHyperspectral UnmixingIdentifiability of Nonnegative Tucker Decompositions -- Part I: Theory
Tensor decompositions have become a central tool in data science, with applications in areas such as data analysis, signal processing, and machine learning. A key property of many tensor decompositions, such as the canon…
Tensor DecompositionNoisy Nonnegative Tucker Decomposition with Sparse Factors and Missing Data
Tensor decomposition is a powerful tool for extracting physically meaningful latent factors from multi-dimensional nonnegative data, and has been an increasing interest in a variety of fields such as image processing, ma…
Tensor Decomposition