The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

D Chen - One of the best experts on this subject based on the ideXlab platform.

  • Optimal biorthogonal Analysis Window function for discrete Gabor transform
    IEEE Transactions on Signal Processing, 1994
    Co-Authors: S. Qian, D Chen
    Abstract:

    Like the DFT/spl minus/a discrete version of the Fourier transform/spl minus/the recently developed discrete Gabor transform (DGT) provides a feasible vehicle to implement the very useful Gabor expansion. In general, the choice of the biorthogonal Window function /spl gamma/(i) is not unique. The authors discuss the solution of /spl gamma/(i) that is optimally close to an arbitrary desired function d(i) when the synthesis Window h(i) and sampling pattern are given. For d(i)=h(i), the resulting /spl gamma//sub opt/(i) directly leads to so-called orthogonal-like DGT. Combining the DGT, they believe that the result presented in this paper is rather significant for digital signal processing.

B Yegnanarayana - One of the best experts on this subject based on the ideXlab platform.

  • extracting formants from short segments of speech using group delay functions
    Conference of the International Speech Communication Association, 2006
    Co-Authors: Joseph M Anand, S Guruprasad, B Yegnanarayana
    Abstract:

    Speech is a non-stationary signal, with the shape of the vocal tract changing over several pitch periods, and also within the open and closed glottis phases. The effect of these changes is reflected in the locations of the formants which correspond to the resonant frequencies of the vocal tract. To observe these changes, the Analysis Window should be small enough (relative to a pitch period), and appropriately anchored. A non-model based method is proposed in this paper to accurately determine formants from short segments (less than a pitch period) of speech signals. It makes use of high resolution properties of group delay function to estimate formants from segments of duration less than a pitch period. The main advantage of this method is its lack of dependence on the parameters of a model. Analysis segments are synchronised with instants of glottal closure, to increase the robustness of formant extraction. Since continuity or additional acoustic-phonetic knowledge are not used, this method is fairly reliable and robust.

  • determination of instants of significant excitation in speech using group delay function
    IEEE Transactions on Speech and Audio Processing, 1995
    Co-Authors: R Smits, B Yegnanarayana
    Abstract:

    A new method for determining the instants of significant excitation in speech signals is proposed. In the paper, significant excitation refers primarily to the instant of glottal closure within a pitch period in voiced speech. The method is based on the global phase characteristics of minimum phase signals. The average slope of the unwrapped phase of the short-time Fourier transform of linear prediction residual is calculated as a function of time. Instants where the phase slope function makes a positive zero-crossing are identified as significant excitations. The method is discussed in a source-filter context of speech production. The method is not sensitive to the characteristics of the filter. The influence of the type, length, and position of the Analysis Window is discussed. The method works well for all types of voiced speech in male as well as female speech but, in all cases, under noise-free conditions only. >

Yu Huang - One of the best experts on this subject based on the ideXlab platform.

  • Time-Frequency Representation Based on an Adaptive Short-Time Fourier Transform
    IEEE Transactions on Signal Processing, 2010
    Co-Authors: Jingang Zhong, Yu Huang
    Abstract:

    In this paper, a new concise algorithm about time-frequency representation (TFR) based on an adaptive short-time Fourier transform (ASTFT) is presented. In this algorithm, the Analysis Window width is equal to the local stationary length which is measured by the instantaneous frequency gradient (IFG) of the signal. And the instantaneous frequency (IF) of the signal is obtained by detecting the ridge of wavelet transform (WT). The ASTFT provides much better performance than conventional TFR algorithms. Furthermore, the algorithm is simpler and more computational efficient than some of other adaptive TFR algorithms proposed previously. Several examples are presented to illustrate its behavior on different kinds of signals and demonstrate its validity.

Hongbo Xie - One of the best experts on this subject based on the ideXlab platform.

  • fuzzy approximate entropy Analysis of chaotic and natural complex systems detecting muscle fatigue using electromyography signals
    Annals of Biomedical Engineering, 2010
    Co-Authors: Jingyi Guo, Hongbo Xie, Yongping Zheng
    Abstract:

    In the present contribution, a complexity measure is proposed to assess surface electromyography (EMG) in the study of muscle fatigue during sustained, isometric muscle contractions. Approximate entropy (ApEn) is believed to provide quantitative information about the complexity of experimental data that is often corrupted with noise, short data length, and in many cases, has inherent dynamics that exhibit both deterministic and stochastic behaviors. We developed an improved ApEn measure, i.e., fuzzy approximate entropy (fApEn), which utilizes the fuzzy membership function to define the vectors’ similarity. Tests were conducted on independent, identically distributed (i.i.d.) Gaussian and uniform noises, a chirp signal, MIX processes, Rossler equation, and Henon map. Compared with the standard ApEn, the fApEn showed better monotonicity, relative consistency, and more robustness to noise when characterizing signals with different complexities. Performance Analysis on experimental EMG signals demonstrated that the fApEn significantly decreased during the development of muscle fatigue, which is a similar trend to that of the mean frequency (MNF) of the EMG signal, while the standard ApEn failed to detect this change. Moreover, fApEn of EMG demonstrated a better robustness to the length of the Analysis Window in comparison with the MNF of EMG. The results suggest that the fApEn of an EMG signal may potentially become a new reliable method for muscle fatigue assessment and be applicable to other short noisy physiological signal Analysis.

  • mean frequency derived via hilbert huang transform with application to fatigue emg signal Analysis
    Computer Methods and Programs in Biomedicine, 2006
    Co-Authors: Hongbo Xie, Zhizhong Wang
    Abstract:

    The mean frequency (MNF) of surface electromyography (EMG) signal is an important index of local muscle fatigue. The purpose of this study is to improve the mean frequency (MNF) estimation. Three methods to estimate the MNF of non-stationary EMG are compared. A novel approach based on Hilbert-Huang transform (HHT), which comprises the empirical mode decomposition (EMD) and Hilbert transform, is proposed to estimate the mean frequency of non-stationary signal. The performance of this method is compared with the two existing methods, i.e. autoregressive (AR) spectrum estimation and wavelet transform method. It is observed that our method shows low variability in terms of robustness to the length of the Analysis Window. The time-varying characteristic of the proposed approach also enables us to accommodate other non-stationary biomedical data Analysis.

Faculteit Elektrotechniek - One of the best experts on this subject based on the ideXlab platform.

  • On the discrete Gabor transform and the discrete Zak transform,” accepted for publication in Signal Process
    2015
    Co-Authors: Mary Jane Bastiaans, Faculteit Elektrotechniek
    Abstract:

    Gabor’s expansion of a discrete-time signal into a set of shifted and modulated versions of an elementary signal or synthesis Window is introduced, along with the inverse operation, i.e. the Gabor transform, which uses an anal-ysis Window that is related to the synthesis Window and with the help of which Gabor’s expansion coefficients can be determined. The restriction to a signal and an Analysis Window that both have finite-support, leads to the con-cept of a discrete Gabor expansion and a discrete Gabor transform. After introduction of the discrete Fourier transform and the discrete Zak transform, it is possible to express the discrete Gabor expansion and the discrete Gabor trans-form as matrix-vector products. Using these matrix-vector products, a relationship between the Analysis win-dow and the synthesis Window is derived. It is shown how this relationship enables us to determine the opti-mum synthesis Window in the sense that it has minimum L2 norm, and it is shown that this optimum synthesis Window resembles best the Analysis Window

  • 1Gabor’s Signal Expansion and the Gabor Transform for a General, Non-Separable Sampling Geometry
    2015
    Co-Authors: Mary Jane Bastiaans, Arno J. Van Leest, Faculteit Elektrotechniek
    Abstract:

    Abstract – Gabor’s signal expansion and the Gabor trans-form are formulated on a general, non-separable time-frequency lattice instead of on the traditional rectangular lattice. The representation of the general lattice is based on the rectangular lattice via a shear operation, which corre-sponds to a description of the general lattice by means of a lattice generator matrix that has the Hermite normal form. The shear operation on the lattice is associated with simple operations on the signal, on the synthesis and the Analysis Window, and on Gabor’s expansion coefficients; these oper-ations consist of multiplications by quadratic phase terms. Following this procedure, the well-known biorthogonality condition for the Window functions in the rectangular sam-pling geometry, can be directly translated to the general case. In the same way, a modified Zak transform can be defined for the non-separable case, with the help of which Gabor’s signal expansion and the Gabor transform can be brought into product forms that are identical to the ones that are well known for the rectangular sampling geometry

  • Gabor’s signal expansion based on a non-orthogonal sampling geometry
    2015
    Co-Authors: Martin J. Bastiaans, Faculteit Elektrotechniek
    Abstract:

    Gabor’s signal expansion and the Gabor transform are formulated on a non-orthogonal time-frequency lattice instead of on the traditional rectangular lattice. The reason for doing so is that a non-orthogonal sampling geometry might be bet-ter adapted to the form of the Window functions (in the time-frequency domain) than an orthogonal one: the set of shifted and modulated versions of the usual Gaussian synthesis Window, for instance, corresponding to circular contour lines in the time-frequency domain, can be arranged more tightly in a hexagonal geometry than in a rectangular one. Oversampling in the Gabor scheme, which is required to have mathematically more attractive properties for the Analysis Window, then leads to better results in combination with less oversampling. The procedure presented in this paper is based on considering the non-orthogonal lattice as a sub-lattice of a denser orthogonal lattice that is oversampled by a rational factor. In doing so, Gabor’s signal expansion on a non-orthogonal lattice can be related to the expan-sion on an orthogonal lattice (restricting ourselves, of course, to only those sam-pling points that are part of the non-orthogonal sub-lattice), and all the techniques that have been derived for rectangular sampling – including an optical means of generating Gabor’s expansion coefficients via the Zak transform in the case of in-teger oversampling – can be used, albeit in a slightly modified form