R
Raviv Raich
Researcher at Oregon State University
Publications - 166
Citations - 3467
Raviv Raich is an academic researcher from Oregon State University. The author has contributed to research in topics: Mean squared error & Statistical manifold. The author has an hindex of 29, co-authored 161 publications receiving 3153 citations. Previous affiliations of Raviv Raich include Industrial Research Limited & Georgia Institute of Technology.
Papers
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Journal ArticleDOI
Orthogonal polynomials for power amplifier modeling and predistorter design
TL;DR: A novel set of orthogonal polynomials is introduced, which can be used for PA as well as predistorter modeling and generally yield better PA modeling accuracy as wellAs predistortion linearization performance.
Journal ArticleDOI
Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach
Forrest Briggs,Balaji Lakshminarayanan,Lawrence Neal,Xiaoli Z. Fern,Raviv Raich,Sarah J. K. Hadley,Adam S. Hadley,Matthew G. Betts +7 more
TL;DR: This work formulates the problem of classifying the set of species present in an audio recording using the multi-instance multi-label (MIML) framework for machine learning, and proposes a MIML bag generator for audio, i.e., an algorithm which transforms an input audio signal into a bag-of-instances representation suitable for use with M IML classifiers.
Proceedings ArticleDOI
Rank-loss support instance machines for MIML instance annotation
TL;DR: This work considers the problem of predicting instance labels while learning from data labeled only at the bag level, and proposes Rank-Loss Support Instance Machines, which optimize a regularized rank-loss objective and can be instantiated with different aggregation models connecting instance- level predictions with bag-level predictions.
Proceedings ArticleDOI
A Hammerstein predistortion linearization design based on the indirect learning architecture
TL;DR: This paper model the PA as a Wiener system and construct a Hammerstein predistorter, obtained using an indirect learning architecture, and linearization performance is demonstrated on a 3-carrier UMTS signal.
Journal ArticleDOI
Orthogonal polynomials for complex Gaussian processes
Raviv Raich,Guotong Zhou +1 more
TL;DR: A novel set of orthogonal polynomials for baseband Gaussian input to replace the conventional polynmials are presented and it is shown how they alleviate the numerical instability problem associated with theventional polynoms.