Institute for Systems Research

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Author: search.filters.author.Baras, John S.Subject: search.filters.subject.speech processing

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    Existence and Construction of Optimal Wavelet Basis for Signal Representation
    (1994) Zhuang, Y.; Baras, John S.; ISR; CSHCN
    We study the problem of choosing the optimal wavelet basis with compact support for signal representation and provide a general algorithm for computing the optimal wavelet basis. We first briefly review the multiresolution property of wavelet decomposition and the conditions for generating a basis of compactly supported discrete wavelets in terms of properties of quadrature mirror filter (QMF) banks. We then parametrize the mother wavelet and scaling function through a set of real coefficients. We further introduce the concept of decomposition entropy as an information measure to describe the distance between the given signal and its projection onto the subspace spanned by the wavelet basis in which the signal is to be reconstructed. The optimal basis for a given signal is obtained through minimizing this information measure. We have obtained explicitly the sensitivity of dilations and shifts of the mother wavelet with respect to the coefficient set. A systematic approach is developed in this paper to derive the information gradient with respect to the parameter set from a given square integrable signal and a discrete basis of wavelets. The existence of the optimal basis for the wavelets has been proven in this paper. a gradient based optimization algorithm is developed for computing the optimal wavelet basis.
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    Optimal Wavelet Basis Selection for Signal Representation
    (1994) Zhuang, Y.; Baras, John S.; ISR; CSHCN
    We study the problem of choosing the optimal wavelet basis with compact support for signal representation and provide a general algorithm for computing the optimal wavelet basis. We first briefly review the multiresolution property of wavelet decomposition and the conditions for generating a basis of compactly supported discrete wavelets in terms of properties of quadrature mirror filter (QMF) banks. We then parametrize the mother wavelet and scaling function through a set of real coefficients. We further introduce the concept of information measure as a distance measure between the signal and its projection onto the subspace spanned by the wavelet basis in which the signal is to be reconstructed. The optimal basis for a given signal is obtained through minimizing this information measure. We have obtained explicitly the sensitivity of dilations and shifts of the mother wavelet with respect to the coefficient set. A systematic approach is developed here to derive the information gradient with respect to the parameter set for a given square integrable signal and the optimal wavelet basis. A gradient based optimazation algorithm is developed in this paper for computing the optimal wavelet basis.
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    Comparison Studies of Several Microphone Robustness Techniques
    (1994) Sonmez, M.K.; Kao, Yu-Hung; Rajasekaran, P.K.; Baras, John S.; ISR
    We study the effectiveness of various microphone robustness techniques from the viewpoint of speech recognition, utilizing the ARPA-sponsored Wall Street Journal (WSJ) data base [1]. Two of the techniques considered are being introduced in this paper: two cepstral normalization algorithms utilizing the artificial neural network techniques Self Organizing Map (SOM) and Learning Vector Quantization (LVQ). The algorithms obtained are low- complexity non-parametric counterparts of the parametric approaches Codeword-dependent Cepstral Normalization (CDCN) and Fixed CDCN (FCDCN). The other techniques considered are Cepstral Mean Normalization (CMN), RASTA, SNR-dependent Cepstral Normalization (SDCN), Interpolated SDCN (ISDCN), CDCN, FCDCN; some of these techniques require one or more of the following information: stereo data, SNR estimate, single microphone data for adaptation, and knowledge of the microphone used for the specific data under test. We determine the effectiveness in several ways: (i) scattergram plot of the speech frame parameter vector (usually a cepstral vector), (ii) adjusted deviation ratio, measured from scattergram, and (iii) correctness of classifying a test vector into a vector code book. All these measures have direct correlation with speech recognition performance, which will be measured with experiments to be conducted.
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    Time-Recursive Computation and Real-Time Parallel Architectures, Part I: Framework
    (1993) Frantzeskakis, Emmanuel N.; Baras, John S.; Liu, K.J. Ray; ISR
    The time-recursive computation has been proved as a particularly useful tool in real-time data compression, in transform domain adaptive filtering and in spectrum analysis. Unlike the FFT based ones, the time-recursive architectures require only local communication. Also, they are modular and regular, thus they are very appropriate for VLSI implementation and they allow high degree of parallelism. In this two part paper, we establish an architectural frame work for parallel time-recursive computation. In part I, we consider a class of linear operators that consists of the discrete time, time invariant, compactly supported, but otherwise arbitrary kernel functions. We show that the structure of the realization of a given linear operator is dictated by the decomposition of the latter with respect to proper basis functions. An optimal way for carrying out this decomposition is demonstrated. The parametric forms of the basis functions are identified and their properties pertinent to the architecture design are studied. A library of architectural building modules capable of realizing these functions is developed. An analysis of the implementation complexity for the aforementioned modules is conducted. Based on this framework, an architecture design procedure is developed in Part II [12] that can be used for routinely obtaining the time-recursive architecture of a given linear operator.