摘要
虽然许多研究人员已认识到三维真实感声音在未来人机交互中的重要地位，但是三维真实感声音在计算机领域的真正实现仍有不少障碍有待克服．基于对声学及心理声学最新研究成果的调查和分析，本文设计并实现了一个基于神经网络方法的ＨＲＴＦ（ｈｅａｄ－ｒｅｌａｔｅｄｔｒａｎｓｆｅｒｆｕｎｃｔｉｏｎ）模型，用于三维真实感声音的生成．模型中的数据可通过网络学习进行重新设置，以满足多种场合的需要．并且，通过神经网络的非线性拟合能力，可以获取空间任意位置的ＨＲＴＦ数据．初步的实验表明了该方法的有效性和正确性．
Although most scholars have realized the importance of 3 D realistic sound embedded in future human computer interaction, there are still many obstacles to overcome. After intensive investigation of the current research situations, an improved HRTF (head related transfer function) model on the basis of neural network method was introduced to generate 3 D realistic sound. The HRTFs data employed can be reset by network training to satisfy a variety of requirements. Furthermore, with the utilization of neural network's nonlinearity approximation ability, all the HRTFs data (including magnitude characteristics and phase characteristics)at any incident angles can be obtained based on discrete sample values.Experiment and verification demonstrate the effectiveness and correctiveness of this scheme.
出处
《软件学报》
EI
CSCD
北大核心
1998年第1期7-13,共7页
Journal of Software
基金
国家863高科技项目基金
浙江省自然科学基金
关键词
声学
仿真
人工神经网络
Artificial neural network, HRTF(head related transfer function), 3 D realistic sound. Class number\ TP391
