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A fast and robust speech noise estimation technique is proposed. The noisy speech is decomposed using a critical-band-rate filterbank so that a perceptual modification of Wiener filtering can be applied in speech denoising. The subband noise estimate is updated adaptively using a smoothing parameter that depends on the estimated signal-to-noise ratio (SNR). This noise estimation technique can give accurate results even at very low signal-tonoise ratios. Speech denoising using perceptually modified Wiener filtering combined with the proposed noise estimation technique gives enhanced speech of good quality.

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The height difference between the ellipsoidal height h and the orthometric height H is called undulation N. The key issue in transforming the global positioning system ͑GPS͒-derived ellipsoidal height to the orthometric height is to determine the undulation value accurately. If the undulation N for a point whose position is determined by a GPS receiver can be estimated in the field, then the GPS-derived three-dimensional geocentric coordinate in WGS-84 can be transformed into a local coordinate system and the orthometric height in real-time. In this paper, algorithms of applying a back-propagation artificial neural network ͑BP ANN͒ to develop a regional grid-based geoid model using GPS data ͑e.g., ellipsoidal height͒ and geodetic leveling data ͑e.g., orthometric height͒ are proposed. In brief, the proposed algorithms include the following steps: ͑1͒ establish the functional relationship between the point's plane coordinates and its undulation using the BP ANN according to the measured GPS data and leveling data; ͑2͒ develop a regional grid-based geoid model using the imaginary grid plane coordinates with a fixed grid interval and the trained BP ANN; ͑3͒ develop an undulation interpolation algorithm to estimate a specific point's undulation using the generated grid-based geoid model; and ͑4͒ estimate the point's undulation in the field and transform the GPS ellipsoidal height into the orthometric height in real-time. Three data sets from the Taiwan region are used to test the proposed algorithms. The test results show that the undulation interpolation estimation accuracy using the generated grid-based geoid is in the order of 2-4 cm. The proposed algorithms and the detailed test results are presented in this paper.