Nonlinear perceptual audio filtering using support vector machines
Simon I. Hill, Patrick J. Wolfe, Peter Julian Rayner · 2002
The perceptually based loss functions for audio filtering used by P.J. Wolfe and S.J. Godsill (see Proc. IEEE ICASSP, vol.2, p.821-4, 2000) are shown to fit well within a complex-valued support vector machine (SVM) framework. SVM regression is extended to the estimation of complex-valued functions, including the derivation of a variant of the sequential minimal optimisation (SMO) algorithm. Audio filters are derived using this, based on an autoregressive (AR) model used for audio and two different Hermitian kernel functions. Results are found to be promising, and further improvements are discussed.