Techniques for Improving the Accuracy of Sinusoidal Tracking

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Author(s)
Marks, Stuart
Gonzalez, Ruben
Year published
2005
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This paper proposes three novel techniques for improving the accuracy of the heuristic sinusoidal tracking algorithm proposed in [1]. When applied to audio coding these techniques extend the traditionally speech coding approach into true wideband audio coding. These techniques provide proper multiresolution sinusoidal tracking, matching across a number of variables instead of frequency alone, and global optimization of these variables across sinusoidal tracks. When these techniques are used together a heuristic tracking algorithm is created which has many of the benefits of tracking algorithms formulated as a Hidden Markov ...
View more >This paper proposes three novel techniques for improving the accuracy of the heuristic sinusoidal tracking algorithm proposed in [1]. When applied to audio coding these techniques extend the traditionally speech coding approach into true wideband audio coding. These techniques provide proper multiresolution sinusoidal tracking, matching across a number of variables instead of frequency alone, and global optimization of these variables across sinusoidal tracks. When these techniques are used together a heuristic tracking algorithm is created which has many of the benefits of tracking algorithms formulated as a Hidden Markov Model (HMM) problem, but at a far reduced computational and implementation complexity.
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View more >This paper proposes three novel techniques for improving the accuracy of the heuristic sinusoidal tracking algorithm proposed in [1]. When applied to audio coding these techniques extend the traditionally speech coding approach into true wideband audio coding. These techniques provide proper multiresolution sinusoidal tracking, matching across a number of variables instead of frequency alone, and global optimization of these variables across sinusoidal tracks. When these techniques are used together a heuristic tracking algorithm is created which has many of the benefits of tracking algorithms formulated as a Hidden Markov Model (HMM) problem, but at a far reduced computational and implementation complexity.
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Conference Title
Internet and Multimedia Systems and Applications ~EuroIMSA 2005~
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Copyright Statement
© 2005 IASTED and ACTA Press. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.
Subject
History and Archaeology