This paper is devoted to the design of the filter for voice signals on the background of acoustic noise. Nonparametric
model of the voice signals is suggested. The results are confirmed by experimental processing of the voice signals.
This paper is devoted to the filtration of acoustic signals on the background of acoustic noise. Signal filtering is done
with the help of a nonlinear analogue of a correlation function - a copula. The copula is estimated with the help of kernel
estimates of the cumulative distribution function. At the second stage we suggest a new procedure of adaptive filtering.
The silence and sound intervals are detected before the filtration with the help of nonparametric algorithm. The results
are confirmed by experimental processing of spoken language signals.
Work is devoted to important topic of acoustic signals processing in a pilot's cabin of aircraft in which the high noise
level is observed. We have investigated heuristic approach of acoustic signal nonlinear filtration. First of all the kernel
estimate of the cumulative distribution function was done. The signal was transformed using the estimate of the
cumulative distribution function as a functional transform. Then measurements of acoustic signals' parameters and an
estimation of their spectral density were done. The estimation was measured by means of fast Fourier transform
procedure with use of window functions. At the second stage the new procedure of the adaptive filtration based on the
Wiener frequency approach has been offered. The estimations of spectra received at the first stage have been thus used.
Results are confirmed by experimental processing of spoken language signals.
A noise sounding signal for an acoustic radar (sodar) is offered. The experimental prototype of a noise sodar is
developed and made. Algorithms of a signal detection, range and velocity measurements are suggested and developed.
The experimental measurements are done. The suggested sodar can be used in meteorology for obtaining information
about direction and velocity of the wind.
Proc. SPIE. 6937, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2007
KEYWORDS: Electronics, Digital signal processing, Statistical analysis, Detection and tracking algorithms, Fourier transforms, Signal processing, Photonics, Signal detection, Stochastic processes, Filtering (signal processing)
In the paper a new approach for estimating of the spoken language sound multivariate probability density is suggested.
It is based on the use of a projection of a random process to the set of random variables, with the probability density
defined as a product of two-dimensional densities. The estimates of two-dimensional probability densities are obtained
with the help of filtering of the two-dimensional empirical characteristic function. Therefore, we are suggesting a
nonparametric estimate of the characteristic function. On the basis of these estimates nonparametric algorithms of sound
classification can be constructed. Examples for the sound probability density function estimates are suggested.
Statistical properties of the images obtained with the help of application of the method of the generalized portrait are
investigated. Kernel estimates of probability density function of the average images are constructed. Statistical
properties of the received images and initial image realizations are investigated. The Markov model for the description
of the generalized portrait is offered. For the Markov model of a random process the kernel estimates of a probability
density function are offered.