Showing papers by "Andreas Spanias published in 1992"

Mixed Fourier/Walsh transform scheme for speech coding at 4.0 kbit/s

Abstract: A mixed Fourier/Walsh transform scheme for speech coding is proposed. A set of harmonically structured frequency components is used to represent narrow-band components of speech. The broadband residual is characterised by a small number of sequency components. The frequencies and sequencies of the transform components are determined by sampling the shorttime Fourier and Walsh transforms, respectively. The magnitudes and phases of the Fourier components and the amplitudes of the Walsh components are determined using an iterative algorithm based on the Gauss-Seidel method. A vector quantisation (VQ) scheme is developed to encode the frequency and the sequency components. Results and subjective evaluations are given for speech coding at 4.0 kbit/s

Block time and frequency domain modified covariance algorithms

Abstract: A block modified covariance algorithm (BMCA) is proposed for autoregressive (AR) parametric spectral estimation. The BMCA can be implemented either in the time or in the frequency domain with the latter being more efficient in high-order AR estimation. Although the BMCA operates on a block of data it allows for both block and sequential (sample-by-sample) updates. The performance of the algorithm is evaluated in terms of computational complexity and convergence characteristics. Results are given for narrowband and broadband processes. >

Autoregressive spectral modeling of Motor Unit Action Potentials: Preliminary findings

Abstract: Autoregressive (AR) spectral modeling of Motor Unit Action Potentials (MUAPs) has been suggested in the past but not thoroughly investigated In this study AR spectral modeling was applied on MUAPs recorded from the biceps muscle The AR parameters were estimated using the Yule-Walker, Burg, and Modified Covariance methods Findings suggest that a similar spectral envelope was obtained by the Burg and the Modified Covariance methods of order 12

Adaptive FIR filtering based on bounded error constraints

Abstract: The performance of adaptive FIR algorithms in the presence of bounded disturbances, in particular, algorithms based on gradient mean squared error (MSE) estimators, gradient estimators with dead-zone thresholds, and set membership estimators, is examined. A simple example of the bursting phenomenon is given, and the algorithms and their properties are described. Analytical results on the performance of these estimators are also given. An algorithm that provides both MSE and instantaneous maximum amplitude error (in the limsup sense) performance guarantees is proposed. >