1,097 research outputs found
On cumulant techniques in speech processing
This paper analyzes applications of cumulant analysis in
speech processing. A special focus is made on different second-order
statistics. A dominant role is played by an integral representation for
cumulants by means of integrals involving cyclic products of kernels
New hos-based parameter estimation methods for speech recognition in noisy environments
The problem of recognition in noisy environments is addressed. Often, a recognition system is used in a noisy environment and there is no possibility of training it with noisy samples. Classical speech analysis techniques are based on second-order statistics and their performance dramatically decreases when noise is present in the signal under analysis. New methods based on higher order statistics (HOS) are applied in a recognition system and compared against the autocorrelation method. Cumulant-based methods show better performance than autocorrelation-based methods for low SNRPeer ReviewedPostprint (published version
Fir system identification using a linear combination of cumulants
A general linear approach to identifying the parameters of a moving average (MA) model from the statistics of the output is developed. It is shown that, under some constraints, the impulse response of the system can be expressed as a linear combination of cumulant slices. This result is then used to obtain a new well-conditioned linear method to estimate the MA parameters of a nonGaussian process. The proposed approach does not require a previous estimation of the filter order. Simulation results show improvement in performance with respect to existing methods.Peer ReviewedPostprint (published version
Detection of multiplicative noise in stationary random processes using second- and higher order statistics
This paper addresses the problem of detecting the presence of colored multiplicative noise, when the information process can be modeled as a parametric ARMA process. For the case of zero-mean multiplicative noise, a cumulant based suboptimal detector is studied. This detector tests the nullity of a specific cumulant slice. A second detector is developed when the multiplicative noise is nonzero mean. This detector consists of filtering the data by an estimated AR filter. Cumulants of the residual data are then shown to be well suited to the detection problem. Theoretical expressions for the asymptotic probability of
detection are given. Simulation-derived finite-sample ROC curves are shown for different sets of model parameters
Zero-Crossing Statistics for Non-Markovian Time Series
In applications spaning from image analysis and speech recognition, to energy
dissipation in turbulence and time-to failure of fatigued materials,
researchers and engineers want to calculate how often a stochastic observable
crosses a specific level, such as zero. At first glance this problem looks
simple, but it is in fact theoretically very challenging. And therefore, few
exact results exist. One exception is the celebrated Rice formula that gives
the mean number of zero-crossings in a fixed time interval of a zero-mean
Gaussian stationary processes. In this study we use the so-called Independent
Interval Approximation to go beyond Rice's result and derive analytic
expressions for all higher-order zero-crossing cumulants and moments. Our
results agrees well with simulations for the non-Markovian autoregressive
model
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