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Paul Rondeau

dblp:86/1227 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2007
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Audio and music processing · 75% Image and video coding · 25%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Audio and music processing › speech coding
linear predictive coding		 0.112007
Multiple-Description Predictive-Vector Quantization With Applications to Low Bit-Rate Speech Coding Over Networks · IEEE Trans. Speech Audio Process. 2007
Audio and music processing › speech coding
line spectral frequency quantization		 0.112007
Multiple-Description Predictive-Vector Quantization With Applications to Low Bit-Rate Speech Coding Over Networks · IEEE Trans. Speech Audio Process. 2007
Image and video coding
multiple description coding		 0.112007
Multiple-Description Predictive-Vector Quantization With Applications to Low Bit-Rate Speech Coding Over Networks · IEEE Trans. Speech Audio Process. 2007
Audio and music processing
speech coding		 0.112007
Multiple-Description Predictive-Vector Quantization With Applications to Low Bit-Rate Speech Coding Over Networks · IEEE Trans. Speech Audio Process. 2007

Methods — techniques the papers use, named apart from their topics

predictive vector quantization · 0.1multiple description vector quantization · 0.1
YearPublicationVenuePosition
2007 Multiple-Description Predictive-Vector Quantization With Applications to Low Bit-Rate Speech Coding Over Networks
abstract
An algorithm for designing linear prediction-based two-channel multiple-description predictive-vector quantizers (MD-PVQs) for packet-loss channels is presented. This algorithm iteratively improves the encoder partition, the set of multiple description codebooks, and the linear predictor for a given channel loss probability, based on a training set of source data. The effectiveness of the designs obtained with the given algorithm is demonstrated using a waveform coding example involving a Markov source as well as vector quantization of speech line spectral pairs
Pradeepa Yahampath, Paul Rondeau
 IEEE Trans. Speech Audio Process.2
2006 DESIGn of Multiple Description Predictive Vector Quantizers
abstract
A design of a robust predictive vector quantizer for packet-loss channels is presented. A linear prediction-based quantizer is considered, in which the prediction residual is quantized by a multiple description vector quantizer, and a simple procedure is developed for iteratively improving the system components, including the linear predictor, for a given channel loss probability. Experimental results obtained by designing quantizers for a Markov source as well as for speech line spectral pairs are presented
Pradeepa Yahampath, Paul Rondeau
 ICASSP (4)2