The spectral subtraction is one of the best methods for elimination of approximate cyclical engine’s noise from degraded speech signal. Here we turn to research about the nonlinear spectral subtraction method and its improved model. After studying the nonlinear method we turn to this method that whether it can improve the quality of enhanced speech signal, propose the short-time spectral subtraction, which needs two inputs. The main input is containing the voice that is corrupted by noise. The other input (noise reference input) contains noise related in some way to that of the main input (background noise). Then use the main input’s frequency spectrum subtract the other input’s frequency spectrum. The results of experiment have proved it’s effective.
The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimodal, cannot represent simultaneous alternative hypotheses. The PF(Particle Filtering) algorithm uses “sequential importance sampling”, previously applied to the posterior of static signals, in which the probability distribution of possible interpretations is represented by a randomly generated set. PF uses learned “sequential Monte Carlo” models, together with practical observations, to propagate and update the random set over time. The result is highly robust tracking of agile motion. Not withstanding the use of stochastic methods, the algorithm runs in near Real-Time.
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