Leah J. Siegel

Parallel processing for computer vision

It has been estimated that processor speeds on the order of 1 to 100 billion operations per second will be required to solve some of the current problems in computer vision. This paper overviews the use of parallel processing techniques for various vision tasks using a parallel processing computer architecture known as PASM (partitionable SIMD/MIMD machine). PASM is a large-scale multimicroprocessor system being designed for image processing and pattern recognition. It can be dynamically reconfigured to operate as one or more independent SIMD (single instruction stream-multiple data stream) and/or MIMD (multiple instruction stream-multiple data stream) machines. This paper begins with a discussion of the computational capabilities required for com-puter vision. It is then shown how parallel processing, and in particular PASM, can be used to meet these needs.

Parallel processing algorithms for linear predictive coding

The use of the SIMD (single instruction stream-multiple data stream) mode of parallelism to perform linear predictive coding analysis is explored. Parallel algorithms for the autocorrelation formulation of linear prediction are presented and analyzed. The algorithms are evaluated in terms of the number of arithmetic operations and interprocessor data transfers required.

Features for the identification of mixed excitation in speech analysis

Features for use in a pattern classification scheme to identify simultaneous periodic and noiselike excitation of a segment of speech are examined. Pattern classification techniques have been applied with considerable success to the problem of classifying a speech segment as voiced or unvoiced. The features that have proven adequate for the voiced/unvoiced decision have not sufficed for the three-way voiced/unvoiced/mixed excitation classification. The incorporation of periodicity measures (e.g. from pitch determination algorithms) into such a pattern classification framework are examined. A variety of features which compare periodicity in different bands of the frequency spectrum are presented.

A decision tree procedure for voiced/Unvoiced/Mixed excitation classification of speech

Pattern classification techniques, which have been successful in determining if a segment of speech is voiced or unvoiced, are used to determine if a speech segment is voiced, unvoiced, or a combination of the two (mixed). The technique employs a binary decision procedure first to determine if the segment is predominantly voiced or unvoiced, and then to determine if the segment is produced by a mixture of the two modes of excitation. The sequence of decisions is structured as a binary tree. Also presented is a method of determining which features of the speech segment are to be used in making each of the binary decisions in the tree. In preliminary tests, classification accuracy of 95% has been obtained.

Highly parallel architectures and algorithms for speech analysis

Highly parallel computer architectures and algorithms for speech analysis operations are surveyed. The classes of architectures considered include SIMD, skewed-SIMD, MIMD, and data flow machines, associative processors, and pipelined systolic and wavefront arrays. Parallel algorithms for digital filtering, FFTs, and linear predictive coding are summarized. System requirements are analyzed in terms of number and complexity of processors, memory requirements, and nature and complexity of inter-processor communications.

Parallel processing for computationally intensive speech analysis operations

Parallel processing is applied to the speech analysis tasks of homomorphic prediction for pole/zero spectral estimation and cepstrum pitch determination. A number of issues bearing on the feasibility of the use of large-scale parallel processing for speech applications are addressed. The speedup over serial implementations is analyzed. The processing task considered consists of a number of distinct algorithms, including FFTs, autocorrelation and covariance LPC analyses, and inverse filtering. The compatibility of adjacent algorithms in the processing sequence is considered in terms of the number of processors used by successive algorithms, the compatibility of the allocation of data to processors, and the type of interprocessor communication needed at the junctures between algorithms. Based on the analyses, the conditions under which the parallel implementation should be most efficient are derived.