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A novel adaptive maximum-likelihood deconvolution algorithm for estimating positive sparse spike trains and its application to speech analysis
Conference paper

A novel adaptive maximum-likelihood deconvolution algorithm for estimating positive sparse spike trains and its application to speech analysis

Chong-Yung Chi and Wu-Ton Chen
1992 IEEE International Workshop on Intelligent Signal Processing and Communication Systems
1992

Abstract

speech analysis and processing;parameter estimation;adaptive filters;filtering and prediction theory
The authors use a positive-mean Bernoulli-Gaussian model for positive sparse spike sequences. They propose a novel adaptive MLD algorithm for estimating positive sparse spike sequences from noisy measurements. Some experimental results with voiced speech data show that the proposed algorithm works well

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