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The Successive Mean Quantization Transform
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2005 (English)Conference paper (Refereed) Published
Abstract [en]

This paper presents the Successive Mean Quantization Transform (SMQT). The transform reveals the organization or structure of the data and removes properties such as gain and bias. The transform is described and applied in speech processing and image processing. The SMQT is considered as an extra processing step for the mel frequency cepstral coefficients commonly used in speech recognition. In image processing the transform is applied in automatic image enhancement and dynamic range compression.

Place, publisher, year, edition, pages
Philadelphia: IEEE , 2005.
National Category
Mathematics Signal Processing
URN: urn:nbn:se:bth-10182ISI: 000229404203108Local ID: diva2:838253
Copyright © 2005 IEEE. Reprinted from IEEE Explore database. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of BTH's products or services Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by sending a blank email message to By choosing to view this document, you agree to all provisions of the copyright laws protecting it. umber=30653Available from: 2012-09-18 Created: 2006-04-11 Last updated: 2015-06-30Bibliographically approved

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Nilsson, MikaelDahl, MattiasClaesson, Ingvar
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