Title :
Nonlinear perceptual audio filtering using support vector machines
Author :
Hill, Simon I. ; Wolfe, Patrick J. ; Rayner, Peter J W
Author_Institution :
Dept. of Eng., Cambridge Univ., UK
fDate :
6/23/1905 12:00:00 AM
Abstract :
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
Keywords :
audio signal processing; autoregressive processes; estimation theory; filtering theory; learning automata; nonlinear filters; optimisation; statistical analysis; Hermitian kernel functions; autoregressive model; complex-valued function estimation; loss functions; nonlinear perceptual audio filtering; sequential minimal optimisation algorithm; support vector machines; Discrete Fourier transforms; Filtering; Filters; Frequency; Kernel; Probability density function; Signal processing; Signal processing algorithms; Speech; Support vector machines;
Conference_Titel :
Statistical Signal Processing, 2001. Proceedings of the 11th IEEE Signal Processing Workshop on
Print_ISBN :
0-7803-7011-2
DOI :
10.1109/SSP.2001.955329
