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Performance Comparison of Neural Networks for HRTFs Approximation

高技术通讯（英文版）2000，Vol.6Issue(1)：14-17,4.

Performance Comparison of Neural Networks for HRTFs Approximation

作者信息

1. Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin 150001, P. R. China;Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin 150001, P. R. China;Beijing Simulation Center, Beijing 100854, P. R. China
折叠

摘要

Abstract

In order to approach to head-related transfer functions (HRTFs), this paper employs and compares three kinds of one-input neural network models, namely, multi-layer perceptron (MLP) networks, radial basis function (RBF) networks and wavelet neural networks (WNN) so as to select the best network model for further HRTFs approximation. Experimental results demonstrate that wavelet neural networks are more efficient and useful.

关键词

Multi-layer perceptron (MLP)/Radial basis function (RBF) networks/Wavelet neural networks (WNN)/Head-related transfer functions (HRTFs)

Key words

Multi-layer perceptron (MLP)/Radial basis function (RBF) networks/Wavelet neural networks (WNN)/Head-related transfer functions (HRTFs)

分类

信息技术与安全科学

引用本文复制引用

..Performance Comparison of Neural Networks for HRTFs Approximation[J].高技术通讯（英文版）,2000,6(1):14-17,4.

基金项目

Supported by National Key Lab.of Control System Simulaticn. （）

高技术通讯（英文版）

OAEI

ISSN：1006-6748

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