A Spectral Conversion Approach to Feature Denoising and Speech Enhancement

In this paper we demonstrate that spectral conversion can be successfully applied to the speech enhancement problem as a feature denoising method. The enhanced spectral features can be used in the context of the Kalman filter for estimating the clean speech signal. In essence, instead of estimating the clean speech features and the clean speech signal using the iterative Kalman filter, we show that is more efficient to initially estimate the clean speech features from the noisy speech features using spectral conversion (using a training speech corpus) and then apply the standard Kalman filter. Our results show an average improvement compared to the iterative Kalman filter that can reach 6 dB in the average segmental output Signal-to-Noise Ratio (SNR), in low input SNR\u27s

Ultrasound image denoising via maximum a posteriori estimation of wavelet coefficients

Abstract — Speckle noise removal by means of digital image processors could improve the diagnostic potential of medical ultrasound. This paper addresses the speckle suppression issue within the framework of wavelet analysis. As a first step of our approach, the logarithm of the original image is decomposed into several scales through a multiresolution analysis employing the 2-D wavelet transform. Then, we design a maximum a posteriori (MAP) estimator, which relies on a recently introduced statistical representation for the wavelet coefficients of ultrasound images [1]. We use an alpha-stable model to develop a blind noise-removal processor that performs a non-linear operation on the data. Finally, we compare our technique to current state-of-the-art denoising methods applied on actual ultrasound images and we find it more effective, both in terms of speckle reduction and signal detail preservation