Pub Date : 1995-05-09DOI: 10.1109/ICASSP.1995.479598
E. Schukat-Talamazzini, J. Hornegger, H. Niemann
Linear discriminant or Karhunen-Loeve transforms are established techniques for mapping features into a lower dimensional subspace. This paper introduces a uniform statistical framework, where the computation of the optimal feature reduction is formalized as a maximum-likelihood estimation problem. The experimental evaluation of this suggested extension of linear selection methods shows a slight improvement of the recognition accuracy.
{"title":"Optimal linear feature transformations for semi-continuous hidden Markov models","authors":"E. Schukat-Talamazzini, J. Hornegger, H. Niemann","doi":"10.1109/ICASSP.1995.479598","DOIUrl":"https://doi.org/10.1109/ICASSP.1995.479598","url":null,"abstract":"Linear discriminant or Karhunen-Loeve transforms are established techniques for mapping features into a lower dimensional subspace. This paper introduces a uniform statistical framework, where the computation of the optimal feature reduction is formalized as a maximum-likelihood estimation problem. The experimental evaluation of this suggested extension of linear selection methods shows a slight improvement of the recognition accuracy.","PeriodicalId":300119,"journal":{"name":"1995 International Conference on Acoustics, Speech, and Signal Processing","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123456752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1995-05-09DOI: 10.1109/ICASSP.1995.480086
B. Porat, B. Friedlander
The paper develops a method of error analysis for Fourier-transform based sinusoidal frequency estimation in the presence of nonrandom interferences. A general error formula is derived, and then specialized to the cases of additive and multiplicative interferences. Approximate error formulas are derived for the case of additive polynomial-phase interference. Finally, an application to error-analysis in estimating the parameters of multiple polynomial-phase signals is discussed in detail.
{"title":"Accuracy analysis of estimation algorithms for parameters of multiple polynomial-phase signals","authors":"B. Porat, B. Friedlander","doi":"10.1109/ICASSP.1995.480086","DOIUrl":"https://doi.org/10.1109/ICASSP.1995.480086","url":null,"abstract":"The paper develops a method of error analysis for Fourier-transform based sinusoidal frequency estimation in the presence of nonrandom interferences. A general error formula is derived, and then specialized to the cases of additive and multiplicative interferences. Approximate error formulas are derived for the case of additive polynomial-phase interference. Finally, an application to error-analysis in estimating the parameters of multiple polynomial-phase signals is discussed in detail.","PeriodicalId":300119,"journal":{"name":"1995 International Conference on Acoustics, Speech, and Signal Processing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125594022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1995-05-09DOI: 10.1109/ICASSP.1995.479550
P. Blanc-Benon, G. Bienvenu
Target motion analysis (TMA) is a basic function in passive sonar, generally using bearings only or bearings and frequency measurements. But due to the use of arrays whose aperture are practically negligible considering the target range, and even if the platform moves itself to yield a "synthetic array", the classical TMA methods take minutes to give an acceptable solution. Hence, this paper presents an enhanced TMA estimator using jointly the bearings and multipath parameters: the differential time-delays and their Doppler shifts. The Cramer-Rao lower bounds are studied for two cases of sound propagation: a constant celerity profile and a bilinear one. They both exhibit advantages in terms of a shorter time to get a given precision of the target parameters: its range, depth, and speed vector.
{"title":"Passive target motion analysis using multipath differential time-delay and differential Doppler shifts","authors":"P. Blanc-Benon, G. Bienvenu","doi":"10.1109/ICASSP.1995.479550","DOIUrl":"https://doi.org/10.1109/ICASSP.1995.479550","url":null,"abstract":"Target motion analysis (TMA) is a basic function in passive sonar, generally using bearings only or bearings and frequency measurements. But due to the use of arrays whose aperture are practically negligible considering the target range, and even if the platform moves itself to yield a \"synthetic array\", the classical TMA methods take minutes to give an acceptable solution. Hence, this paper presents an enhanced TMA estimator using jointly the bearings and multipath parameters: the differential time-delays and their Doppler shifts. The Cramer-Rao lower bounds are studied for two cases of sound propagation: a constant celerity profile and a bilinear one. They both exhibit advantages in terms of a shorter time to get a given precision of the target parameters: its range, depth, and speed vector.","PeriodicalId":300119,"journal":{"name":"1995 International Conference on Acoustics, Speech, and Signal Processing","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126714961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1995-05-09DOI: 10.1109/ICASSP.1995.480315
A. Bist
Addresses the problem of approximating the quantization noise spectra when a Gauss-Markov process is input to a sigma-delta modulator. The process is modeled using a state space approach. Fine quantization approximations are used to derive expressions for the output spectrum. Results similar to those of Gray's [1990] analysis are obtained
{"title":"An approximate analysis of sigma-delta modulation of a Gauss-Markov process","authors":"A. Bist","doi":"10.1109/ICASSP.1995.480315","DOIUrl":"https://doi.org/10.1109/ICASSP.1995.480315","url":null,"abstract":"Addresses the problem of approximating the quantization noise\u0000spectra when a Gauss-Markov process is input to a sigma-delta modulator.\u0000The process is modeled using a state space approach. Fine quantization\u0000approximations are used to derive expressions for the output spectrum.\u0000Results similar to those of Gray's [1990] analysis are obtained","PeriodicalId":300119,"journal":{"name":"1995 International Conference on Acoustics, Speech, and Signal Processing","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115220592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1995-05-09DOI: 10.1109/ICASSP.1995.479391
Sabine Deligne, F. Bimbot
The multigram model assumes that language can be described as the output of a memoryless source that emits variable-length sequences of words. The estimation of the model parameters can be formulated as a maximum likelihood estimation problem from incomplete data. We show that estimates of the model parameters can be computed through an iterative expectation-maximization algorithm and we describe a forward-backward procedure for its implementation. We report the results of a systematical evaluation of multigrams for language modeling on the ATIS database. The objective performance measure is the test set perplexity. Our results show that multigrams outperform conventional n-grams for this task.
{"title":"Language modeling by variable length sequences: theoretical formulation and evaluation of multigrams","authors":"Sabine Deligne, F. Bimbot","doi":"10.1109/ICASSP.1995.479391","DOIUrl":"https://doi.org/10.1109/ICASSP.1995.479391","url":null,"abstract":"The multigram model assumes that language can be described as the output of a memoryless source that emits variable-length sequences of words. The estimation of the model parameters can be formulated as a maximum likelihood estimation problem from incomplete data. We show that estimates of the model parameters can be computed through an iterative expectation-maximization algorithm and we describe a forward-backward procedure for its implementation. We report the results of a systematical evaluation of multigrams for language modeling on the ATIS database. The objective performance measure is the test set perplexity. Our results show that multigrams outperform conventional n-grams for this task.","PeriodicalId":300119,"journal":{"name":"1995 International Conference on Acoustics, Speech, and Signal Processing","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116118184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1995-05-09DOI: 10.1109/ICASSP.1995.479953
Yong Zhang, J. Fessler, N. Clinthorne, W. Rogers
Single photon emission computed tomographic images (SPECT) have relatively poor resolution. In an attempt to improve SPECT image quality, many methods have been developed for including anatomic information, extracted from higher resolution, structurally correlated magnetic resonance images (MRI), into the SPECT reconstruction process. These methods provide improved SPECT reconstruction accuracy if the anatomic information is perfectly correlated with the SPECT functional information. However there exist mismatches between MRI anatomical structures and SPECT functional structures due to different imaging mechanisms. It has been reported that if the MR structures are applied into SPECT, the mismatched part will cause artifacts. The paper describes a joint estimation approach which unifies MR information extraction and SPECT reconstruction processes to avoid these artifacts. Both qualitative and quantitative evaluations show that the method improves the SPECT reconstruction where the MR information matches and is robust to mismatched MR information.
{"title":"Incorporating MRI region information into SPECT reconstruction using joint estimation","authors":"Yong Zhang, J. Fessler, N. Clinthorne, W. Rogers","doi":"10.1109/ICASSP.1995.479953","DOIUrl":"https://doi.org/10.1109/ICASSP.1995.479953","url":null,"abstract":"Single photon emission computed tomographic images (SPECT) have relatively poor resolution. In an attempt to improve SPECT image quality, many methods have been developed for including anatomic information, extracted from higher resolution, structurally correlated magnetic resonance images (MRI), into the SPECT reconstruction process. These methods provide improved SPECT reconstruction accuracy if the anatomic information is perfectly correlated with the SPECT functional information. However there exist mismatches between MRI anatomical structures and SPECT functional structures due to different imaging mechanisms. It has been reported that if the MR structures are applied into SPECT, the mismatched part will cause artifacts. The paper describes a joint estimation approach which unifies MR information extraction and SPECT reconstruction processes to avoid these artifacts. Both qualitative and quantitative evaluations show that the method improves the SPECT reconstruction where the MR information matches and is robust to mismatched MR information.","PeriodicalId":300119,"journal":{"name":"1995 International Conference on Acoustics, Speech, and Signal Processing","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116146279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1995-05-09DOI: 10.1109/ICASSP.1995.480029
A. Ferrari, R. Lorion, G. Alengrin
The paper treats the problem of ambiguity resolution using non-uniform sampling. This problem occurs for Doppler estimation in coherent pulsed Doppler radar. In the paper, the authors study the case where the duration between two samples is a linear function of time: quadratic sampling. Assuming that the continuous signal is stationary, the sampled signal will be non-stationary. The autocorrelation of this signal is derived and the Wigner distribution of the sampled signal related to the spectrum of the continuous signal. As a consequence, a time frequency relief of the signal will verify symmetries. These constraints, assuming an AR evolutive model for the sampled signal and band limitation for the continuous signal, allow the derivation of a particular time varying model for the samples. An associated estimation algorithm, leading to the unfolded spectrum is then proposed.
{"title":"A parametric model for the quadratic sampling of a bandlimited signal","authors":"A. Ferrari, R. Lorion, G. Alengrin","doi":"10.1109/ICASSP.1995.480029","DOIUrl":"https://doi.org/10.1109/ICASSP.1995.480029","url":null,"abstract":"The paper treats the problem of ambiguity resolution using non-uniform sampling. This problem occurs for Doppler estimation in coherent pulsed Doppler radar. In the paper, the authors study the case where the duration between two samples is a linear function of time: quadratic sampling. Assuming that the continuous signal is stationary, the sampled signal will be non-stationary. The autocorrelation of this signal is derived and the Wigner distribution of the sampled signal related to the spectrum of the continuous signal. As a consequence, a time frequency relief of the signal will verify symmetries. These constraints, assuming an AR evolutive model for the sampled signal and band limitation for the continuous signal, allow the derivation of a particular time varying model for the samples. An associated estimation algorithm, leading to the unfolded spectrum is then proposed.","PeriodicalId":300119,"journal":{"name":"1995 International Conference on Acoustics, Speech, and Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122730452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1995-05-09DOI: 10.1109/ICASSP.1995.480088
R. Kakarala, J. Cadzow
A procedure for estimating the parameters associated with a linear phase signal is developed. When the data being modelled is composed of a linear phase signal corrupted by additive Gaussian noise the approach taken results in maximum-likelihood estimates of the linear phase parameters. These estimates are useful for detecting and estimating the presence of symmetry in both one and two dimensions. The effectiveness of the estimates is tested on both synthetic and real images.
{"title":"Estimation of phase for noisy linear phase signals","authors":"R. Kakarala, J. Cadzow","doi":"10.1109/ICASSP.1995.480088","DOIUrl":"https://doi.org/10.1109/ICASSP.1995.480088","url":null,"abstract":"A procedure for estimating the parameters associated with a linear phase signal is developed. When the data being modelled is composed of a linear phase signal corrupted by additive Gaussian noise the approach taken results in maximum-likelihood estimates of the linear phase parameters. These estimates are useful for detecting and estimating the presence of symmetry in both one and two dimensions. The effectiveness of the estimates is tested on both synthetic and real images.","PeriodicalId":300119,"journal":{"name":"1995 International Conference on Acoustics, Speech, and Signal Processing","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114054303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1995-05-09DOI: 10.1109/ICASSP.1995.480673
V. Krishnamurthy, H. Poor
Krishnamurthy and Mareels presented a parameter estimation algorithm called the binary series estimation algorithm (BSEA) for Gaussian auto-regressive (AR) time series given 1-bit quantized noisy measurements. The present authors carry out an asymptotic analysis of the BSEA for Gaussian AR models. In particular, from a central limit theorem they obtain expressions for the asymptotic covariances of the parameter estimates. From this they: (1) Present an algorithm for estimating the order of an AR series from one-bit quantized measurements. (2) Theoretically they justify why BSEA can yield better estimates than the Yule-Walker methods in some cases.
{"title":"Asymptotic analysis of an algorithm for identification of quantized AR time-series","authors":"V. Krishnamurthy, H. Poor","doi":"10.1109/ICASSP.1995.480673","DOIUrl":"https://doi.org/10.1109/ICASSP.1995.480673","url":null,"abstract":"Krishnamurthy and Mareels presented a parameter estimation algorithm called the binary series estimation algorithm (BSEA) for Gaussian auto-regressive (AR) time series given 1-bit quantized noisy measurements. The present authors carry out an asymptotic analysis of the BSEA for Gaussian AR models. In particular, from a central limit theorem they obtain expressions for the asymptotic covariances of the parameter estimates. From this they: (1) Present an algorithm for estimating the order of an AR series from one-bit quantized measurements. (2) Theoretically they justify why BSEA can yield better estimates than the Yule-Walker methods in some cases.","PeriodicalId":300119,"journal":{"name":"1995 International Conference on Acoustics, Speech, and Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114186647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1995-05-09DOI: 10.1109/ICASSP.1995.480336
Junghsi Lee, V. J. Mathews
This paper introduces output-error LMS bilinear filters with stability monitoring. Bilinear filters are recursive nonlinear systems that belong to the class of polynomial systems. Because of the feedback structure, such models are able to represent many nonlinear systems efficiently. However, the usefulness of adaptive bilinear filters is greatly restricted unless they are guaranteed to perform in a stable manner. A stability monitoring scheme is proposed to overcome the stability problem. The paper concludes with simulation results that demonstrate the usefulness of our technique.
{"title":"Output-error LMS bilinear filters with stability monitoring","authors":"Junghsi Lee, V. J. Mathews","doi":"10.1109/ICASSP.1995.480336","DOIUrl":"https://doi.org/10.1109/ICASSP.1995.480336","url":null,"abstract":"This paper introduces output-error LMS bilinear filters with stability monitoring. Bilinear filters are recursive nonlinear systems that belong to the class of polynomial systems. Because of the feedback structure, such models are able to represent many nonlinear systems efficiently. However, the usefulness of adaptive bilinear filters is greatly restricted unless they are guaranteed to perform in a stable manner. A stability monitoring scheme is proposed to overcome the stability problem. The paper concludes with simulation results that demonstrate the usefulness of our technique.","PeriodicalId":300119,"journal":{"name":"1995 International Conference on Acoustics, Speech, and Signal Processing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114407046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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