Abstract
In this paper, the problem of the online modeling of nonlinear speech signals is addressed. In particular, the goal of this work is to provide a nonlinear model yielding the best tradeoff between performance results and required computational resources. Functional link adaptive filters were proved to be an effective model for this problem, providing the best performance when trigonometric expansion is used as a nonlinear transformation. Here, a different functional expansion is adopted based on the Chebyshev polynomials in order to reduce the overall computational complexity of the model, while achieving good results in terms of perceived quality of processed speech. The proposed model is assessed in the presence of nonlinearities for both simulated and real speech signals.
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Comminiello, D., Scarpiniti, M., Scardapane, S., Parisi, R., Uncini, A. (2019). A Low-Complexity Linear-in-the-Parameters Nonlinear Filter for Distorted Speech Signals. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Neural Advances in Processing Nonlinear Dynamic Signals. WIRN 2017 2017. Smart Innovation, Systems and Technologies, vol 102. Springer, Cham. https://doi.org/10.1007/978-3-319-95098-3_10
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