PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Optimal filtering of dynamics in short-time features for music organization
Jeronimo Arenas-Garcia, Jan Larsen, Lars Kai Hansen and Anders Meng
In: ISMIR 2006, 8-12 Oct 2006, Victoria, Canada.

Abstract

Lately, there has been 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 in- formation. In this work, a novel method, denoted as the Positive Constrained Orthonormalized Partial Least Squares (POPLS), is proposed. Working on the periodograms of MFCCs time series, this supervised method finds optimal filters which pick out the most discriminative temporal in- formation for any music organization task. Two examples are presented in the paper, the first being a simple proof-of- concept, where an altosax with and without vibrato is mod- elled. A more complex 11 music genre classification setup is also investigated to illustrate the robustness and validity of the proposed method on larger datasets. Both experiments showed the good properties of our method, as well as su- perior performance when compared to a fixed filter bank approach suggested previously in the MIR literature. We think that the proposed method is a natural step towards a customized MIR application that generalizes well to a wide range of different music organization tasks.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:2114
Deposited By:Anders Meng
Deposited On:26 May 2006