Feature set augmentation for enhancing the performance of a non-intrusive quality predictor

Abstract

A non-intrusive quality predictor constitutes a mapping from signal features to a (typically one dimensional) representation of the perceived quality. Assuming that the regression model performing the mapping is suited to the data, the performance of the predictor largely depends on how well the parameters of this regression model can be inferred from the training data. In situations where the training data is scarce, model performance is degraded due to over-fitting. The effects of over-fitting can be mitigated by feature selection but the model performance remains low due to the insufficiently representative training data. The objective we pursue is to enhance the performance of a quality predictor by augmenting the feature set with the output of a pre-trained quality predictor. This approach introduces an implicit dependence of the regression model parameters on a larger amount of training data. In view of the increasing usage of speech signals with higher bandwidth, and the dearth of training data for such signals, an augmentation of particular interest is that of a wide-band feature set with a narrow-band quality prediction. Experimental results for additive noise and non-linear distortions encountered in hearing aids, using quality labels from an intrusive quality predictor, illustrate the performance enhancement capabilities of the proposed approach.