http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
비선형 특징추출 기법에 의한 머리전달함수 ( HRTE ) 의 저차원 모델링 및 합성
서상원(Sang Won Suh),김기홍(Ki Hong Kim),김현석(Ki Hong Kim),김현빈(Hyun Suk Kim),이의택(Ee Taek Lee) 한국정보처리학회 2000 정보처리학회논문지 Vol.7 No.5
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 data 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 regression 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 directional information in feature extraction yielded more stable results in synthesizing general HRTFs not included in the model training.