Open Access

Radial Basis Function Networks for Conversion of Sound Spectra

EURASIP Journal on Advances in Signal Processing20012001:435352

https://doi.org/10.1155/S1110865701000117

Received: 5 April 2000

Published: 1 March 2001

Abstract

In many advanced signal processing tasks, such as pitch shifting, voice conversion or sound synthesis, accurate spectral processing is required. Here, the use of Radial Basis Function Networks (RBFN) is proposed for the modeling of the spectral changes (or conversions) related to the control of important sound parameters, such as pitch or intensity. The identification of such conversion functions is based on a procedure which learns the shape of the conversion from few couples of target spectra from a data set. The generalization properties of RBFNs provides for interpolation with respect to the pitch range. In the construction of the training set, mel-cepstral encoding of the spectrum is used to catch the perceptually most relevant spectral changes. Moreover, a singular value decomposition (SVD) approach is used to reduce the dimension of conversion functions. The RBFN conversion functions introduced are characterized by a perceptually-based fast training procedure, desirable interpolation properties and computational efficiency.

Keywords

sound transformations sinusoidal representation RBFNs spectral processing

Authors’ Affiliations

(1)
Dipartimento di Elettronica e Informatica (DEI), Università di Padova

Copyright

© Drioli 2001