Abstract
Forward masking models have been used successfully in speech enhancement and audio coding. Presently, forward masking thresholds are estimated using simplified masking models which have been used for audio coding and speech enhancement applications. In this paper, an accurate approximation of forward masking threshold estimation 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. Moreover, a parallel implementation of the speech enhancement algorithm was developed using Matlab parallel computing toolbox.
Highlights
Forward masking is a time domain phenomenon in which a masker precedes the signal in time
Functional models of the forward masking effect of the human auditory system have recently been used with success in speech and audio coding to provide more efficient signal compression [2, 3]
The masking threshold to noise ratio (MNR) in each subband can be calculated by using the ratio of a short-term average forward masking threshold, Pm n, and an estimate of the noise floor level, Qm n as given in Eqn (8)
Summary
Forward masking is a time domain phenomenon in which a masker precedes the signal in time. Further refinement of the model requires software that can do curve-fitting of multi-dimensional data. For this purpose, we utilise neural network to better approximate forward masking threshold. Speech enhancement algorithm exploiting temporal masking properties of human auditory system has a very high computation requirement, especially when the noisy speech signal is long or the number of subbands is high. Recent advances in multi-core system make it a natural choice and viable option for solving high computation requirements of the speech enhancement algorithm. The objective of this paper is two-folds: to evaluate the performance of our forward masking model in terms of enhanced speech quality and to implement and evaluate parallel speech enhancement algorithm on a multi-core system.
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