Constructing Signal from Imperfect Data without Prior Information and Training Data

Abstract

In many situations, the signal can be disrupted not only by noise but also by missing data. Many works present deep learning techniques to solve the noise and missing-data problems. These techniques give good efficiency for removing adulterated things. However, many deep learning techniques do not give good efficiency in computational time because these methods require large architecture and training data via prior information. Moreover, prior information may be lacking in some situations. Therefore, we present the algorithm which comprises the least squares estimation to solve the missing-data problem and the penalized least squares regression (PLSR) for solving the noise problem. The proposed algorithm is based on the novel channel model which is not similar to the traditional form of additive white Gaussian noise (AWGN). Here, the proposed method is not similar to deep learning methods, so this method gives very good efficiency in computational time. Moreover, the proposed shrinkage function which is built by the PLSR is the generalized form of the well-known shrinkage function presented in the past. Finally, we use the speech, electrocardiogram (ECG) and synthetic signals corrupted by adulterated things for testing the proposed algorithm. Experimental results show that the proposed method gives good efficiency for creating data.