Nonlinear Perceptual Audio Filtering Using Support Vector Machines
نویسندگان
چکیده
In this paper, the perceptually based loss functions for audio filtering used by Wolfe and Godsill [1] are shown to fit well within a complex-valued Support Vector Machine (SVM) framework. SVM regression is extended to 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.
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تاریخ انتشار 2001