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B. S. Daya Sagar - One of the best experts on this subject based on the ideXlab platform.

  • A composite Signal Subspace speech classifier
    Signal Processing, 2007
    Co-Authors: B. S. Daya Sagar
    Abstract:

    Recently, a speech model inspired by Signal Subspace methods was proposed for a speech classifier. In using Subspace information to characterize the speech Signal, Subspace trajectories in the form of the right singular vectors of the measurement matrices are obtained. Signal classification is thereafter accomplished by a minimum-distance rule with noteworthy results. This paper extends the foregoing approach by organizing the vector trajectories into matrices. The matrices so obtained are the reduced-rank approximation of the sample correlation matrices. A new dissimilarity measure in the Frobenius norm is correspondingly proposed for the matrix trajectories. Simulation results of the proposed composite Signal Subspace classifier in an isolated digit speech recognition problem reveal an improved performance over its predecessor. Additionally, the results also show the proposed classifier retaining the white noise robustness of the original design.

  • A Signal Subspace approach for speech modelling and classification
    Signal Processing, 2007
    Co-Authors: Alan W. C. Tan, M. V. C. Rao, B. S. Daya Sagar
    Abstract:

    In this paper, a speech classifier inspired by the Signal Subspace approach is developed. A novel Signal Subspace speech model is initially obtained via a rank reducing Subspace decomposition algorithm that is based on the SVD. Motivated by the assumption that the speech Signal comprises of short term dynamics that are slowly changing, it follows that the Signal Subspace of the speech Signal is likewise slowly changing. The proposed Signal Subspace model aims to characterize the Subspace dynamics using a family of Subspace trajectories. In particular, each Subspace trajectory is a sequence of vectors that traces the dynamics of a rank-one Subspace in time. An assembly of these trajectories, henceforth, specifies the progression of the embedded Signal Subspace. To construct the Signal Subspace classifier, prototype elements in the form of the Signal Subspace models are determined for every Signal class. A minimum-distance rule with a distance measure that resembles an energy difference function is subsequently applied in the actual classification task. Simulation of the proposed Signal Subspace classifier in an isolated digit speech recognition problem reveals promising results.

  • Robust Signal Subspace Speech Classifier
    IEEE Signal Processing Letters, 2007
    Co-Authors: Alan W. C. Tan, M. V. C. Rao, B. S. Daya Sagar
    Abstract:

    A speech model inspired by the Signal Subspace approach was recently proposed as a speech classifier with modest results. The method entails, in general, the assemblage of a set of Subspace trajectories that consist of the right singular vectors of measurement matrices of the Signal under consideration. Given an unknown Signal, a simple distortion measure then applies in the classification procedure to pick the best matched class prototype. This letter examines the issue of robustness in the Subspace classification scheme. Borrowing an important result on noisy measurement matrices, this letter formally establishes the notion of robustness in Subspace classification and proceeds to propose a class of robust distortion measures for Signal Subspace models. Simulation results of Subspace classifiers implementing the new distortion measures in an isolated digit speech recognition problem reveal no degradation in recognition accuracy, even under low SNR conditions.

John Aasted Sorensen - One of the best experts on this subject based on the ideXlab platform.

  • experimental comparison of Signal Subspace based noise reduction methods
    International Conference on Acoustics Speech and Signal Processing, 1999
    Co-Authors: Peter Soren Kirk Hansen, Per Christian Hansen, S D Hansen, John Aasted Sorensen
    Abstract:

    The Signal Subspace approach for non-parametric speech enhancement is considered. Several algorithms have been proposed in the literature but only partly analyzed. Here, the different algorithms are compared, and the emphasis is put onto the limiting factors and practical behavior of the estimators. Experimental results show that the Signal Subspace approach may lead to a significant enhancement of the Signal to noise ratio of the output Signal.

  • ICASSP - Experimental comparison of Signal Subspace based noise reduction methods
    1999 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999
    Co-Authors: Peter Soren Kirk Hansen, Per Christian Hansen, S D Hansen, John Aasted Sorensen
    Abstract:

    The Signal Subspace approach for non-parametric speech enhancement is considered. Several algorithms have been proposed in the literature but only partly analyzed. Here, the different algorithms are compared, and the emphasis is put onto the limiting factors and practical behavior of the estimators. Experimental results show that the Signal Subspace approach may lead to a significant enhancement of the Signal to noise ratio of the output Signal.

Peter Soren Kirk Hansen - One of the best experts on this subject based on the ideXlab platform.

  • experimental comparison of Signal Subspace based noise reduction methods
    International Conference on Acoustics Speech and Signal Processing, 1999
    Co-Authors: Peter Soren Kirk Hansen, Per Christian Hansen, S D Hansen, John Aasted Sorensen
    Abstract:

    The Signal Subspace approach for non-parametric speech enhancement is considered. Several algorithms have been proposed in the literature but only partly analyzed. Here, the different algorithms are compared, and the emphasis is put onto the limiting factors and practical behavior of the estimators. Experimental results show that the Signal Subspace approach may lead to a significant enhancement of the Signal to noise ratio of the output Signal.

  • ICASSP - Experimental comparison of Signal Subspace based noise reduction methods
    1999 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999
    Co-Authors: Peter Soren Kirk Hansen, Per Christian Hansen, S D Hansen, John Aasted Sorensen
    Abstract:

    The Signal Subspace approach for non-parametric speech enhancement is considered. Several algorithms have been proposed in the literature but only partly analyzed. Here, the different algorithms are compared, and the emphasis is put onto the limiting factors and practical behavior of the estimators. Experimental results show that the Signal Subspace approach may lead to a significant enhancement of the Signal to noise ratio of the output Signal.

Mostafa Kaveh - One of the best experts on this subject based on the ideXlab platform.

  • ICASSP - Performance of narrowband Signal-Subspace processing
    ICASSP '86. IEEE International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: H. Wang, Mostafa Kaveh
    Abstract:

    This paper presents an analytical evaluation of detection (determination of the number of sources) and estimation performances of narrowband Signal-Subspace processing for multiple-source direction finding. The probabilities of underestimating and overestimating the number of sources are derived, under asymptotic conditions and around threshold region, in terms of the choice of a penalty function and Signal, noise and array parameters for the cases of at most two closely-spaced sources in the spatially white noise. A scalar measure is introduced for the evaluation of the quality of the estimated Signal-Subspace. Based on the statistics of this measure performance thresholds are demonstrated for the Signal-to-noise ratio, angle separation and correlation between two equipowered sources.

  • ICASSP - Sensitivity and performance analysis of coherent Signal-Subspace processing for multiple wideband sources
    ICASSP '85. IEEE International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: H. Wang, Mostafa Kaveh
    Abstract:

    This paper presents some of the statistical properties of a new Signal-Subspace approach to the estimation of the angles-of-arrival (AOA) of several wideband plane waves received in noise. This approach is denoted as the Coherent Signal-Subspace Method (CSM). The statistics that are presented include the bias and variance of the AOA estimates as well as a statistical measure of the similarity between the true and estimated coherent Signal-Subspaces.

H.l. Van Trees - One of the best experts on this subject based on the ideXlab platform.

  • a Signal Subspace approach for speech enhancement
    International Conference on Acoustics Speech and Signal Processing, 1993
    Co-Authors: Y. Ephraim, H.l. Van Trees
    Abstract:

    A perceptually based linear Signal estimator for enhancing speech Signals degraded by uncorrelated additive noise is developed. The estimator is designed by minimizing the Signal distortion while maintaining the residual noise level below some given threshold. The estimator is shown to be a Wiener filter with adjustable input noise level. This level is determined by the threshold of the permissible residual noise. The estimator is implemented using the Signal Subspace approach. The vector space of the noisy Signal is decomposed into a Signal Subspace and complementary orthogonal noise Subspace. Estimation is performed from vectors in the Signal Subspace only, since the orthogonal Subspace does not contain Signal information. The proposed estimator is shown to be a refinement of a version of the spectral subtraction Signal estimator. The latter estimator is shown to be asymptotically optimal for stationary Signal and noise in the linear minimum mean square error sense. >

  • ICASSP (2) - A Signal Subspace approach for speech enhancement
    IEEE International Conference on Acoustics Speech and Signal Processing, 1993
    Co-Authors: Y. Ephraim, H.l. Van Trees
    Abstract:

    A perceptually based linear Signal estimator for enhancing speech Signals degraded by uncorrelated additive noise is developed. The estimator is designed by minimizing the Signal distortion while maintaining the residual noise level below some given threshold. The estimator is shown to be a Wiener filter with adjustable input noise level. This level is determined by the threshold of the permissible residual noise. The estimator is implemented using the Signal Subspace approach. The vector space of the noisy Signal is decomposed into a Signal Subspace and complementary orthogonal noise Subspace. Estimation is performed from vectors in the Signal Subspace only, since the orthogonal Subspace does not contain Signal information. The proposed estimator is shown to be a refinement of a version of the spectral subtraction Signal estimator. The latter estimator is shown to be asymptotically optimal for stationary Signal and noise in the linear minimum mean square error sense. >

  • ICASSP - A spectrally-based Signal Subspace approach for speech enhancement
    1995 International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: Y. Ephraim, H.l. Van Trees
    Abstract:

    The Signal Subspace approach for enhancing speech Signals degraded by uncorrelated additive noise is studied. The underlying principle is to decompose the vector space of the noisy Signal into a Signal plus noise Subspace and a noise Subspace. Enhancement is performed by removing the noise Subspace and estimating the clean Signal from the remaining Signal Subspace. The decomposition can theoretically be performed by applying the Karhunen-Loeve transform to the noisy Signal. Linear estimation of the clean Signal is performed using a perceptually meaningful estimation criterion. The estimator is designed by minimizing Signal distortion for a fixed desired spectrum of the residual noise. This criterion enables masking of the residual noise by the speech Signal. The filter is implemented as a gain function which modifies the KLT components corresponding to the Signal Subspace. The gain function is solely dependent on the desired spectrum of the residual noise. Listening tests indicate that 14 out of 16 listeners strongly preferred the proposed approach over the spectral subtraction approach.