Signal processing modelling

Figure 11.8 shows the signal-processing model of the blown bottle. The funny shaped function in the middle models the tendency of air to flow into or out of the bottle as a function of the differential pressure on the piston. It is similar to the reed reflection coefficient table shown in Figure 11.4, but the... 

A. Mohammad-Djafari M. Nikolova. Eddy current tomography using binary markov model. Signal Processing, 49 119-132, 1996. 

O. Venard. Eddy current tomography a bayesian approach with a compound weak membrane-beta prior model. In Advances in Signal Processing for Non Destructive Evaluation of Materials, 1997. 

R. F. Lyon and E. P. Loeb, Isolated Digit Recognition Experiments with a Cochlear Model, in Proceedings International Conference on Acoustics Speech and Signal Processing—ICASSP-87, Dallas, Texas (April 1987). 

The experienced catalytic chemist or chemical reaction engineer will immediately recognize that the study of a new catalytic reaction system using an in situ spectroscopy, has a great deal in common with the concepts of inverse problems and system identification. First, there is a physical system which cannot be physically disassembled, and the researcher seeks to identify a model for the chemistry involved. The inverse in situ spectroscopic problem can be denoted by Eq. (2). Secondly, the physical system evolves in time and spectroscopic measurements as a function of time are a must. There are realistic limitations to the spectroscopic measurements performed. For this reason as well as for various other reasons, the inverse problem is ill-posed (see Section 4.3.6). Third, signal processing will be needed to filter and correct the raw data, and to obtain a model of the system. The ability to have the individual pure component spectra of the species present in... 

Fido s extreme sensitivity benefits potential users in many ways beyond direct detection needs. For applications where sensitivity is not as critical, reasonable trade-offs can be made in system design. For example, the use of less sophisticated optics can reduce cost and allow the system to be more rugged, while lower complexity in sample collection and signal processing supports portability through lightweight, low-power models. 

Depalle et al., 1993] Depalle, P., Garcia, G., and Rodet, X. (1993). Analysis of sound for additive synthesis Tracking of partials using hidden markov models. In Proc. 1993 Workshop on Applications of signal Processing to Audio and Acoustics, Mohonk Mountain House, New Paltz, NY. 

Kates, 1991b] Kates, J. (1991b). A time-domain digital cochlear model. IEEE Trans. Signal Processing, 39( 12) 2573—2592. 

Laroche, 1994] Laroche, J. (1994). Multichannel cxcitation/filtcr modeling of percussive sounds with application to piano. IEEE Trans, on Acoustics, Speech, and Signal Processing, ASSP-02(2) 329-345. 

Laroche et al., 1993b] Laroche, J., Stylianou, Y., and Moulines, E. (1993b). HNM A simple, efficient harmonic plus noise model for speech. Proc. IEEE Workshop Appl. of Signal Processing to Audio and Acoustics. 

Macon and Clements, 1996] Macon, M. and Clements, M. (1996). Speech concatenation and synthesis using an overlap-add sinusoidal model. In Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing., volume 1, pages 361-364, Atlanta, GA. 

Petersen and Boll, 1981] Petersen, T. L. and Boll, S. F. (1981). Acoustic noise suppression in the context of a perceptual model. In Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, volume 1086-1088. 

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