PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Multivariate Analysis and Kernel Methods for Music Data Analysis
Jerónimo Arenas-García, Anders Meng, Kaare Brandt Petersen and Lars Kai Hansen
In: NIPS workshop on Advances in Models for Acoustic Processing, 8 Dec 2006, Whistler.

Abstract

There is an increasing interest in customizable methods for organizing music collections. Relevant music characterization can be obtained from short-time features, but it is not obvious how to combine them to get useful information. First, the relevant information might not be evident at the short-time level, and these features have to be combined at a larger temporal level into a new feature vector in order to capture the relevant information. Second, we need to learn a model for the new features that generalizes well to new data. In this contribution, we will study how multivariate analysis (MVA) and kernel methods can be of great help in this task. More precisely, we will present two modified versions of a MVA method known as Orthonormalized Partial Least Squares (OPLS), one of them being a kernel extension, that are well-suited for discovering relevant dynamics in large music collections. The performance of both schemes will be illustrated in a music genre classification task.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:2691
Deposited By:Anders Meng
Deposited On:22 November 2006