Discovering auditory objects through non-negativity constraints

Abstract

We present a novel method for discovering auditory objects from scenes in a self-organized manner. Our approach is using non-negativity constraints to find the building elements of the input. Surprisingly, although devoid of any statistical measures, this approach discovers independent elements in the scene similarly to previously reported methods employing ICA algorithms. The use of non-negativity constraints makes this work best suited for spectral magnitude analysis and provides a fiarly robust method for discovery and extraction of auditory objects from scenes. SAPA 2004 This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Abstract We present a novel method for discovering auditory objects from scenes in a self-organized manner. Our approach is using non-negativity constraints to find the building elements of the input. Surprisingly, although devoid of any statistical measures, this approach discovers independent elements in the scene similarly to previously reported methods employing ICA algorithms. The use of non-negativity constraints makes this work best suited for spectral magnitude analysis and provides a fairly robust method for discovery and extraction of auditory objects from scenes.

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