비선형 특징추출 기법에 의한 머리전달함수(HRTF)의 저차원 모델링 및 합성

Low Dimensional Modeling and Synthesis of Head-Related Transfer Function (HRTF) Using Nonlinear Feature Extraction Methods

  • 서상원 (한국전자통신연구원 가상현실연구개발센터) ;
  • 김기홍 (한국전자통신연구원 가상현실연구개발센터) ;
  • 김현석 (한국전자통신연구원 가상현실연구개발센터) ;
  • 김현빈 (한국전자통신연구원) ;
  • 이의택 (한국전자통신연구원)
  • 발행 : 2000.05.01

초록

For the implementation of 3D Sound Localization system, the binaural filtering by HRTFs is generally employed. But the HRTF filter is of high order and its coefficients for all directions have to be stored, which imposes a rather large memory requirement. To cope with this, research works have centered on obtaining low dimensional HRTF representations without significant loss of information and synthesizing the original HRTF efficiently, by means of feature extraction methods for multivariate dat including PCA. In these researches, conventional linear PCA was applied to the frequency domain HRTF data and using relatively small number of principal components the original HRTFs could be synthesized in approximation. In this paper we applied neural network based nonlinear PCA model (NLPCA) and the nonlinear PLS repression model (NLPLS) for this low dimensional HRTF modeling and analyze the results in comparison with the PCA. The NLPCA that performs projection of data onto the nonlinear surfaces showed the capability of more efficient HRTF feature extraction than linear PCA and the NLPLS regression model that incorporates the direction information in feature extraction yielded more stable results in synthesizing general HRTFs not included in the model training.

키워드

참고문헌

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