Pub Date : 2002-11-07DOI: 10.1109/NNSP.2002.1030058
H. Sawada, S. Araki, R. Mukai, S. Makino
This paper presents a method for blind source separation using several separating subsystems whose sensor spacing and filter length can be configured individually. Each subsystem is responsible for source separation of an allocated frequency range. With this mechanism, we can use appropriate sensor spacing as well as filter length for each frequency range. We obtained better separation performance than with the conventional method by using a wide sensor spacing and a long filter for a low frequency range, and a narrow sensor spacing and a short filter for a high frequency range.
{"title":"Blind source separation with different sensor spacing and filter length for each frequency range","authors":"H. Sawada, S. Araki, R. Mukai, S. Makino","doi":"10.1109/NNSP.2002.1030058","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030058","url":null,"abstract":"This paper presents a method for blind source separation using several separating subsystems whose sensor spacing and filter length can be configured individually. Each subsystem is responsible for source separation of an allocated frequency range. With this mechanism, we can use appropriate sensor spacing as well as filter length for each frequency range. We obtained better separation performance than with the conventional method by using a wide sensor spacing and a long filter for a low frequency range, and a narrow sensor spacing and a short filter for a high frequency range.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130966061","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 : 2002-11-07DOI: 10.1109/NNSP.2002.1030043
N. Neretti, N. Intrator
We present a general framework for the design of a mother wavelet best adapted to a specific signal or to a class of signals. The filter's coefficients are obtained via optimization of a smooth objective function. We develop an unconstrained gradient-based optimization algorithm for a discrete wavelet transform. The algorithm is extended to the joint optimization of the mother wavelet and of the wavelet packets basis.
{"title":"An adaptive approach to wavelet filters design","authors":"N. Neretti, N. Intrator","doi":"10.1109/NNSP.2002.1030043","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030043","url":null,"abstract":"We present a general framework for the design of a mother wavelet best adapted to a specific signal or to a class of signals. The filter's coefficients are obtained via optimization of a smooth objective function. We develop an unconstrained gradient-based optimization algorithm for a discrete wavelet transform. The algorithm is extended to the joint optimization of the mother wavelet and of the wavelet packets basis.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122809336","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 : 2002-11-07DOI: 10.1109/NNSP.2002.1030016
C. G. Molina, J. Mullikin
This paper introduces a new algorithm for DNA sequence analysis, based on the use of a reference DNA sequence for the estimation of base positions, and a probabilistic modelling of trace peaks. The new algorithm has been applied to long read-length DNA sequences and its performance has been compared to the base-calling program Phred. The results reported in this paper, after cross-matching with a finished consensus, show a significant improvement by the new algorithm in the final sequence read-length and in the number of correct bases extracted from DNA traces.
{"title":"A probabilistic approach for long read-length DNA sequence analysis","authors":"C. G. Molina, J. Mullikin","doi":"10.1109/NNSP.2002.1030016","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030016","url":null,"abstract":"This paper introduces a new algorithm for DNA sequence analysis, based on the use of a reference DNA sequence for the estimation of base positions, and a probabilistic modelling of trace peaks. The new algorithm has been applied to long read-length DNA sequences and its performance has been compared to the base-calling program Phred. The results reported in this paper, after cross-matching with a finished consensus, show a significant improvement by the new algorithm in the final sequence read-length and in the number of correct bases extracted from DNA traces.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124946836","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 : 2002-11-07DOI: 10.1109/NNSP.2002.1030062
R. Mutihac, M. Hulle
The performance of six neuromorphic adaptive structurally different algorithms was analyzed in blind separation of independent artificially generated signals using the stationary linear independent component analysis (ICA) model. The estimated independent components were assessed and compared aiming to rank the neural ICA implementations. All algorithms were run with different contrast functions, which were optimally selected on the basis of maximizing the sum of individual negentropies of the network outputs. Both subGaussian and superGaussian one-dimensional time series were employed throughout the numerical simulations.
{"title":"Neural network implementations of independent component analysis","authors":"R. Mutihac, M. Hulle","doi":"10.1109/NNSP.2002.1030062","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030062","url":null,"abstract":"The performance of six neuromorphic adaptive structurally different algorithms was analyzed in blind separation of independent artificially generated signals using the stationary linear independent component analysis (ICA) model. The estimated independent components were assessed and compared aiming to rank the neural ICA implementations. All algorithms were run with different contrast functions, which were optimally selected on the basis of maximizing the sum of individual negentropies of the network outputs. Both subGaussian and superGaussian one-dimensional time series were employed throughout the numerical simulations.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122113667","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 : 2002-11-07DOI: 10.1109/NNSP.2002.1030076
S. Ikbal, K. Weber, H. Bourlard
We present an HMM2 based method for speaker normalization. Introduced as an extension of hidden Markov model (HMM), HMM2 differentiates itself from the regular HMM in terms of the emission density modeling, which is done by a set of state-dependent HMMs working in the feature vector space. The emission modeling HMM aims at maximizing the likelihood through optimal alignment of its states across the feature components. This property makes it potentially useful to speaker normalization, when applied to spectrum. With the alignment information we get, it is possible to normalize the speaker related variations through piecewise linear warping of frequency axis of the spectrum. In our case, (emission modeling) HMM based spectral warping is employed in the feature extraction block of regular HMM framework for normalizing the speaker related variabilities. After brief description of HMM2, we present the general approach towards HMM2-based speaker normalization and show, through preliminary experiments, the pertinence of the approach.
{"title":"Speaker normalization using HMM2","authors":"S. Ikbal, K. Weber, H. Bourlard","doi":"10.1109/NNSP.2002.1030076","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030076","url":null,"abstract":"We present an HMM2 based method for speaker normalization. Introduced as an extension of hidden Markov model (HMM), HMM2 differentiates itself from the regular HMM in terms of the emission density modeling, which is done by a set of state-dependent HMMs working in the feature vector space. The emission modeling HMM aims at maximizing the likelihood through optimal alignment of its states across the feature components. This property makes it potentially useful to speaker normalization, when applied to spectrum. With the alignment information we get, it is possible to normalize the speaker related variations through piecewise linear warping of frequency axis of the spectrum. In our case, (emission modeling) HMM based spectral warping is employed in the feature extraction block of regular HMM framework for normalizing the speaker related variabilities. After brief description of HMM2, we present the general approach towards HMM2-based speaker normalization and show, through preliminary experiments, the pertinence of the approach.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129583875","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 : 2002-11-07DOI: 10.1109/NNSP.2002.1030070
P. Moallem, K. Faez
The reduction of the search region in stereo correspondence can increase the performance of the matching process, in the context of execution time and accuracy. For edge-based stereo matching, we establish the relationship between the search space and parameters like relative displacement of the edges, the disparity under consideration, the image resolution, the CCD dimensions and the focal length of the stereo system. Then, we propose a novel matching strategy for the edge-based stereo. Afterward, we develop a fast algorithm for edge based-stereo with combination of the obtained matching strategy and the multiresolution technique using the Haar wavelet. Considering conventional multiresolution techniques, we show that the execution time of our algorithm is decreased more than 36%. Moreover, the matching rate and the accuracy are increased. Theoretical investigation and experimental results show that our algorithm has a very good performance, therefore this new algorithm is very suitable for fast edge-based stereo applications like stereo robot vision.
{"title":"Fast edge-based stereo matching algorithm based on search space reduction","authors":"P. Moallem, K. Faez","doi":"10.1109/NNSP.2002.1030070","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030070","url":null,"abstract":"The reduction of the search region in stereo correspondence can increase the performance of the matching process, in the context of execution time and accuracy. For edge-based stereo matching, we establish the relationship between the search space and parameters like relative displacement of the edges, the disparity under consideration, the image resolution, the CCD dimensions and the focal length of the stereo system. Then, we propose a novel matching strategy for the edge-based stereo. Afterward, we develop a fast algorithm for edge based-stereo with combination of the obtained matching strategy and the multiresolution technique using the Haar wavelet. Considering conventional multiresolution techniques, we show that the execution time of our algorithm is decreased more than 36%. Moreover, the matching rate and the accuracy are increased. Theoretical investigation and experimental results show that our algorithm has a very good performance, therefore this new algorithm is very suitable for fast edge-based stereo applications like stereo robot vision.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131198369","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 : 2002-11-07DOI: 10.1109/NNSP.2002.1030030
M. Kimura, Kazumi Saito, N. Ueda
We propose a growing network model and its learning algorithm. Unlike the conventional scale-free models, we incorporate community structure, which is an important characteristic of many real-world networks including the Web. In our experiments, we confirmed that the proposed model exhibits a degree distribution with a power-law tail, and our method can precisely estimate the probability of a new link creation from data without community information. Moreover, by introducing a measure of dynamic hub-degrees, we could predict the change of hub-degrees between communities.
{"title":"Modeling of growing networks with communities","authors":"M. Kimura, Kazumi Saito, N. Ueda","doi":"10.1109/NNSP.2002.1030030","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030030","url":null,"abstract":"We propose a growing network model and its learning algorithm. Unlike the conventional scale-free models, we incorporate community structure, which is an important characteristic of many real-world networks including the Web. In our experiments, we confirmed that the proposed model exhibits a degree distribution with a power-law tail, and our method can precisely estimate the probability of a new link creation from data without community information. Moreover, by introducing a measure of dynamic hub-degrees, we could predict the change of hub-degrees between communities.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123671868","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 : 2002-11-07DOI: 10.1109/NNSP.2002.1030017
S. Mukhopadhyay, Changhong Tang, Jeffrey R. Huang, Mulong Yu, M. Palakal
Classification of genetic sequence data available in public and private databases is an important problem in using, understanding, retrieving, filtering and correlating such large volumes of information. Although a significant amount of research effort is being spent internationally on this problem, very few studies exist that compare different classification approaches in terms of an objective and quantitative classification performance criterion. In this paper, we present experimental studies for classification of genetic sequences using both unsupervised and supervised approaches, focusing on both computational effort as well as a suitably defined classification performance measure. The results indicate that both unsupervised classification using the Maximin algorithm combined with FASTA sequence alignment algorithm and supervised classification using artificial neural network have good classification performance, with the unsupervised classification performs better and the supervised classification performs faster. A trade-off between the quality of classification and the computational efforts exists. The utilization of these classifiers for retrieval, filtering and correlation of genetic information as well as prediction of functions and structures will be logical future directions for further research.
{"title":"A comparative study of genetic sequence classification algorithms","authors":"S. Mukhopadhyay, Changhong Tang, Jeffrey R. Huang, Mulong Yu, M. Palakal","doi":"10.1109/NNSP.2002.1030017","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030017","url":null,"abstract":"Classification of genetic sequence data available in public and private databases is an important problem in using, understanding, retrieving, filtering and correlating such large volumes of information. Although a significant amount of research effort is being spent internationally on this problem, very few studies exist that compare different classification approaches in terms of an objective and quantitative classification performance criterion. In this paper, we present experimental studies for classification of genetic sequences using both unsupervised and supervised approaches, focusing on both computational effort as well as a suitably defined classification performance measure. The results indicate that both unsupervised classification using the Maximin algorithm combined with FASTA sequence alignment algorithm and supervised classification using artificial neural network have good classification performance, with the unsupervised classification performs better and the supervised classification performs faster. A trade-off between the quality of classification and the computational efforts exists. The utilization of these classifiers for retrieval, filtering and correlation of genetic information as well as prediction of functions and structures will be logical future directions for further research.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123822336","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 : 2002-11-07DOI: 10.1109/NNSP.2002.1030021
K. Yosui, T. Kurihara, K. Wada, T. Souma, Takashi Matsumoto
This paper proposes a Rao-Blackwellised sequential Monte Carlo (RBSMC) scheme for on-line learning with feedforward neural nets. The proposed algorithm is tested against an example and the performance is compared with those of the conventional sequential Monte Carlo as well as the extended Kalman filter (EKF). The proposed scheme outperforms those conventional algorithms.
{"title":"Bayesian on-line learning: a sequential Monte Carlo with Rao-Blackwellization","authors":"K. Yosui, T. Kurihara, K. Wada, T. Souma, Takashi Matsumoto","doi":"10.1109/NNSP.2002.1030021","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030021","url":null,"abstract":"This paper proposes a Rao-Blackwellised sequential Monte Carlo (RBSMC) scheme for on-line learning with feedforward neural nets. The proposed algorithm is tested against an example and the performance is compared with those of the conventional sequential Monte Carlo as well as the extended Kalman filter (EKF). The proposed scheme outperforms those conventional algorithms.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"42 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127990085","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 : 2002-11-07DOI: 10.1109/NNSP.2002.1030023
F. Deleus, P. D. Mazière, M. Hulle
We apply the principle of causal networks to develop a new tool for connectivity analysis in functional magnetic resonance imaging (fMRI). The connections between active brain regions are modelled as causal relationships in a causal network. The causal networks are based on the notion of d-separation in a graph-theoretic context or, equivalently, on the notion of conditional independence in a statistical context. Since relationships between brain regions are believed to be nonlinear in nature, we express the conditional dependencies between the brain regions' activities in terms of conditional mutual information. The density estimates needed for computing the conditional mutual information are obtained with topographic maps, trained with the kernel-based maximum entropy rule (kMER).
{"title":"Functional connectivity modelling in fMRI based on causal networks","authors":"F. Deleus, P. D. Mazière, M. Hulle","doi":"10.1109/NNSP.2002.1030023","DOIUrl":"https://doi.org/10.1109/NNSP.2002.1030023","url":null,"abstract":"We apply the principle of causal networks to develop a new tool for connectivity analysis in functional magnetic resonance imaging (fMRI). The connections between active brain regions are modelled as causal relationships in a causal network. The causal networks are based on the notion of d-separation in a graph-theoretic context or, equivalently, on the notion of conditional independence in a statistical context. Since relationships between brain regions are believed to be nonlinear in nature, we express the conditional dependencies between the brain regions' activities in terms of conditional mutual information. The density estimates needed for computing the conditional mutual information are obtained with topographic maps, trained with the kernel-based maximum entropy rule (kMER).","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114862518","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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