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In this paper, we investigated the enhancement of speech by applying an optimal adaptive low-pass filter supervised by neural network. The corruption of speech due to the presence of additive noise causes its degradation in quality and intelligibility. To filter this distorted signal in its spatial representation is a hard task. This task is more difficult to realize if the distortion are caused by colored noise. In addition using a static filter is not efficient due to the speech signal variability. In the same sentence a phoneme can change in shape and amplitude. For these constraints, we propose to apply a low-pass filter with Gaussian core supervised by neural networks. Filtering strength changes continuously with the phoneme variation to generate a variable filter that change over the whole sentence.

Image filtering, which removes or reduces noises from the contaminated images, is an important task in image processing. This paper presents a novel approach to the problem of noise reduction for gray-scale images. The proposed technique is able to remove the noise component, while adapting itself to the local noise intensity. In this way, the proposed algorithm can be considered as a modification of the median filter driven by fuzzy membership functions. Experimental results are compared to static median filter by numerical measures and visual inspection. As was expected, the new filter shows better performances.