The development of a forward masking model using neural networks and its application to speech enhancement

Forward masking models have been used successfully in speech enhancement and audio coding. Currently, simple forward masking models have been employed to predict the forward masking threshold. In this paper, an accurate approximation of forward masking threshold using neural networks is proposed. A performance comparison to the other existing masking models in speech enhancement application is presented. Objective measures using PESQ demonstrates that our proposed forward masking model, provides significant improvements (5-15%) over four existing models, when tested with speech signals corrupted by various noises at very low signal to noise ratios.