Showing papers in "IEEE Assp Magazine in 1986"
TL;DR: The purpose of this tutorial paper is to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.
Abstract: The basic theory of Markov chains has been known to mathematicians and engineers for close to 80 years, but it is only in the past decade that it has been applied explicitly to problems in speech processing. One of the major reasons why speech models, based on Markov chains, have not been developed until recently was the lack of a method for optimizing the parameters of the Markov model to match observed signal patterns. Such a method was proposed in the late 1960's and was immediately applied to speech processing in several research institutions. Continued refinements in the theory and implementation of Markov modelling techniques have greatly enhanced the method, leading to a wide range of applications of these models. It is the purpose of this tutorial paper to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.
4,546 citations
Journal Article•
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305 citations
TL;DR: This paper provides an overview of those techniques in signal processing which appear to be most promising for use in spread spectrum communications.
Abstract: Spread spectrum communication systems use a transmission bandwidth that is typically much larger than that which is required to transmit the information. The advantage of using this excess bandwidth is that the system becomes less sensitive to many types of interference. To further aid the spread spectrum receiver in suppressing interference, various signal processing techniques can be employed. In this paper, we will provide an overview of those techniques in signal processing which appear to be most promising for use in spread spectrum communications.
78 citations
TL;DR: Modeling of Physical systems can be decomposed into four subproblems: representation, measurement, estimation, and validation, and this paper shows how this decomposition can be applied to the reflection seismology problems of inversion and deconvolution.
Abstract: Modeling of Physical systems can be decomposed into four subproblems: representation, measurement, estimation, and validation. This paper shows how this decomposition can be applied to the reflection seismology problems of inversion and deconvolution, and that reflection seismology is a very rich field for practitioners of parameter estimation, system identification, and signal processing.
20 citations
TL;DR: In this paper, a tutorial article on the application of geometrical vector space concepts for deriving the rapidly converging, reduced computation structures known as fast recursive least squares (RLS) adaptive filters is presented.
Abstract: This is a tutorial article on the application of geometrical vector space concepts for deriving the rapidly converging, reduced computation structures known as fast recursive least squares (RLS) adaptive filters. Since potential applications of fast RLS, such as speech coding [1] and echo, cancellation [2], have been previously examined in the ASSP Magazine, this article focuses instead on an intuitive geometrical approach to deriving these fast RLS filters for linear prediction. One purpose of this article is to keep the required mathematics at a minimum and instead highlight the properties of the fast RLS filters through geometrical interpretation. The geometrical vector space concepts in this article are then applied to deriving the very important fast RLS structure known as the fast transversal filter (FTF).
17 citations