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Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop

Sept. 30 1991-Oct. 1 1991

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Displaying Results 1 - 25 of 64
  • Neural Networks for Signal Processing. Proceedings of the 1991 IEEE Workshop (Cat. No.91TH0385-5)

    Publication Year: 1991
    Request permission for commercial reuse | PDF file iconPDF (27 KB)
    Freely Available from IEEE
  • Improving learning rate of neural tree networks using thermal perceptrons

    Publication Year: 1991, Page(s):90 - 100
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (428 KB)

    A new neural network called the neural tree network (NTN) is a combination of decision trees and multi-layer perceptrons (MLP). The NTN grows the network as opposed to MLPs. The learning algorithm for growing NTNs is more efficient that standard decision tree algorithms. Simulation results have shown that the NTN is superior in performance to both decision trees and MLPs. A new NTN learning algori... View full abstract»

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  • An effective method for visual pattern recognition

    Publication Year: 1991, Page(s):217 - 225
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (256 KB)

    An effective method for neural network based visual pattern recognition is presented. It is shown that it can be successfully used for visual recognition of deformed letters. The main advantages of the presented method are its intuitive appeal, simple implementation and analytical justification View full abstract»

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  • Speech recognition using time-warping neural networks

    Publication Year: 1991, Page(s):337 - 346
    Cited by:  Patents (49)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (396 KB)

    The author proposes a time-warping neural network (TWNN) for phoneme-based speech recognition. The TWNN is designed to accept phonemes with arbitrary duration, whereas conventional phoneme recognition networks have a fixed-length input window. The purpose of this network is to cope with not only variability of phoneme duration but also time warping in a phoneme. The proposed network is composed of... View full abstract»

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  • Dimensionality reduction of dynamical patterns using a neural network

    Publication Year: 1991, Page(s):256 - 265
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (364 KB)

    To recognize speech with dynamical features, one should use feature parameters including dynamical changing patterns, that is, time sequential patterns. The K-L expansion has been used to reduce the dimensionality of time sequential patterns. This method changes the axes of feature parameter space linearly by minimizing the error between original and reconstructed parameters. In this paper, the di... View full abstract»

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  • Adaline with adaptive recursive memory

    Publication Year: 1991, Page(s):101 - 110
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (448 KB)

    The authors present a generalization of Widrow's adaptive linear combiner with an adaptive recursive memory. Expressions for memory depth and resolution are derived. The LMS procedure is extended to adapt the memory depth and resolution so as to match the signal characteristics. The particular memory structure, gamma memory, was originally developed as part of a neural net model for temporal proce... View full abstract»

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  • The outlier process [picture processing]

    Publication Year: 1991, Page(s):60 - 69
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (384 KB)

    The authors discuss the problem of detecting outliers from a set of surface data. They start from the Bayes approach and the assumption that surfaces are piecewise smooth and corrupted by a combination of white Gaussian and salt and pepper noise. They show that such surfaces can be modelled by introducing an outlier process that is capable of `throwing away' data. They make use of mean field techn... View full abstract»

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  • Fingerprint recognition using neural network

    Publication Year: 1991, Page(s):226 - 235
    Cited by:  Papers (12)  |  Patents (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (420 KB)

    The authors describe a neural network based approach for automated fingerprint recognition. Minutiae are extracted from the fingerprint image via a multilayer perceptron (MLP) classifier with one hidden layer. The backpropagation learning technique is used for its training. Selected features are represented in a special way such that they are simultaneously invariant under shift, rotation and scal... View full abstract»

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  • A neural network pre-processor for multi-tone detection and estimation

    Publication Year: 1991, Page(s):580 - 588
    Cited by:  Papers (3)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (296 KB)

    A parallel bank of neural networks each trained in a specific band of the spectrum is proposed as a pre-processor for the detection and estimation of multiple sinusoids at low SNRs. A feedforward neural network model in the autoassociative mode, trained using the backpropagation algorithm, is used to construct this sectionized spectrum analyzer. The key concept behind this scheme is that, the netw... View full abstract»

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  • A hybrid continuous speech recognition system using segmental neural nets with hidden Markov models

    Publication Year: 1991, Page(s):347 - 356
    Cited by:  Papers (2)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (448 KB)

    The authors present the concept of a `segmental neural net' (SNN) for phonetic modeling in continuous speech recognition (CSR) and demonstrate how than can be used with a multiple hypothesis (or N-Best) paradigm to combine different CSR systems. In particular, they have developed a system that combines the SNN with a hidden Markov model (HMM) system. They believe that this is the first system inco... View full abstract»

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  • A critical overview of neural network pattern classifiers

    Publication Year: 1991, Page(s):266 - 275
    Cited by:  Papers (24)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (444 KB)

    A taxonomy of neural network pattern classifiers is presented which includes four major groupings. Global discriminant classifiers use sigmoid or polynomial computing elements that have `high' nonzero outputs over most of their input space. Local discriminant classifiers use Gaussian or other localized computing elements that have `high' nonzero outputs over only a small localized region of their ... View full abstract»

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  • On adaptive acquisition of spoken language

    Publication Year: 1991, Page(s):422 - 431
    Cited by:  Patents (13)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (400 KB)

    At present, automatic speech recognition technology is based upon constructing models of the various levels of linguistic structure assumed to compose spoken language. These models are either constructed manually or automatically trained by example. A major impediment is the cost, or even the feasibility, of producing models of sufficient fidelity to enable the desired level of performance. The pr... View full abstract»

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  • Learned representation normalization: attention focusing with multiple input modules

    Publication Year: 1991, Page(s):111 - 120
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (524 KB)

    A large, multi-modular neural network can be envisaged for use in a complex, multi-task application. The optimum data representation for each sub-task of such an application is often unknown and different from the optimum data representation for the other sub-tasks. A method is needed that allows a network that contains several alternate input representations to learn to focus its attention on the... View full abstract»

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  • A mapping approach for designing neural sub-nets

    Publication Year: 1991, Page(s):70 - 79
    Cited by:  Papers (2)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (300 KB)

    Several investigators have constructed back-propagation (BP) neural networks by assembling smaller, pre-trained building blocks. This approach leads to faster training and provides a known topology for the network. The authors carry this process down one additional level, by describing methods for mapping given functions to sub-blocks. First, polynomial approximations to the desired function are f... View full abstract»

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  • Improving generalization performance in character recognition

    Publication Year: 1991, Page(s):198 - 207
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (340 KB)

    One test of a new training algorithm is how well the algorithm generalizes from the training data to the test data. A new neural net training algorithm termed double backpropagation improves generalization in character recognition by minimizing the change in the output due to small changes in the input. This is accomplished by minimizing the normal energy term found in backpropagation and an addit... View full abstract»

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  • A comparison of second-order neural networks to transform-based method for translation- and orientation-invariant object recognition

    Publication Year: 1991, Page(s):236 - 245
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (388 KB)

    Neural networks can use second-order neurons to obtain invariance to translations in the input pattern. Alternatively transform methods can be used to obtain translation invariance before classification by a neural network. The authors compare the use of second-order neurons to various translation-invariant transforms. The mapping properties of second-order neurons are compared to those of the gen... View full abstract»

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  • Design of a digital VLSI neuroprocessor for signal and image processing

    Publication Year: 1991, Page(s):606 - 615
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (336 KB)

    An efficient processing element for data/image processing has been designed. Detailed communication networks, instruction sets and circuit blocks are created for ring-connected and mesh-connected systolic arrays for the retrieving and learning phases of the neural network operations. 800 processing elements can be implemented in 3.75 cm×3.75 cm chip by using the 0.5 μm CMOS technology fro... View full abstract»

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  • Fuzzy tracking of multiple objects

    Publication Year: 1991, Page(s):589 - 592
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (160 KB)

    The authors have applied a previously developed MLANS neural network to the problem of tracking multiple objects in heavy clutter. In their approach the MLANS performs a fuzzy classification of all objects in multiple frames in multiple classes of tracks and random clutter. This novel approach to tracking using an optimal classification algorithm results in a dramatic improvement of performance: t... View full abstract»

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  • Connectionist speaker normalization and its applications to speech recognition

    Publication Year: 1991, Page(s):357 - 366
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (448 KB)

    Speaker normalization may have a significant impact on both speaker-adaptive and speaker-independent speech recognition. In this paper, a codeword-dependent neural network (CDNN) is presented for speaker normalization. The network is used as a nonlinear mapping function to transform speech data between two speakers. The mapping function is characterized by two important properties. First, the asse... View full abstract»

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  • Word recognition based on the combination of a sequential neural network and the GPDM discriminative training algorithm

    Publication Year: 1991, Page(s):376 - 384
    Cited by:  Papers (1)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (280 KB)

    The authors propose an isolated-word recognition method based on the combination of a sequential neural network and a discriminative training algorithm using the Generalized Probabilistic Descent Method (GPDM). The sequential neural network deals with the temporal variation of speech by dynamic programming, and the GPDM discriminative training algorithm is used to discriminate easily confused word... View full abstract»

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  • Workstation-based phonetic typewriter

    Publication Year: 1991, Page(s):279 - 288
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (460 KB)

    The author presents a general description of his `phonetic typewriter' system that transcribes unlimited speech into orthographically correct text. The purpose of this paper is to motivate certain choices made in the partitioning of the problem into tasks and describe their implementation. The combination of algorithms he has selected has proven effective for well-articulated dictation in a phonem... View full abstract»

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  • Supervised and unsupervised feature extraction from a cochlear model for speech recognition

    Publication Year: 1991, Page(s):460 - 469
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (468 KB)

    The authors explore the application of a novel classification method that combines supervised and unsupervised training, and compare its performance to various more classical methods. The authors first construct a detailed high dimensional representation of the speech signal using Lyon's cochlear model and then optimally reduce its dimensionality. The resulting low dimensional projection retains t... View full abstract»

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  • Concept formation and statistical learning in nonhomogeneous neural nets

    Publication Year: 1991, Page(s):30 - 39
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (276 KB)

    The authors present an analysis of complex nonhomogeneous neural nets, an adaptive statistical learning algorithm, and the potential use of these types of systems to perform a general sensor fusion problem. The three main points are the following. First, an extension to the theory of statistical neurodynamics is introduced to include the analysis of complex nonhomogeneous neuron pools consisting o... View full abstract»

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  • Nonlinear prediction of speech signals using memory neuron networks

    Publication Year: 1991, Page(s):395 - 404
    Cited by:  Papers (7)  |  Patents (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (364 KB)

    The authors present a feed-forward neural network architecture that can be used for nonlinear autoregressive prediction of multivariate time-series. It uses specialized neurons (called memory neurons) to store past activations of the network in an efficient fashion. The network learns to be a nonlinear predictor of the appropriate order to model temporal waveforms of speech signals. Arrays of such... View full abstract»

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  • Vector quantisation with a codebook-excited neural network

    Publication Year: 1991, Page(s):432 - 441
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (468 KB)

    An alternative model named a codebook-excited neural network has been proposed for source coding or vector quantisation. Two advantages of this model are that the memory information between source frames can easily be taken into account by recurrent connections and that the number of network connections is independent of the transmission rate. The simulations have also shown its good quantisation ... View full abstract»

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