Study on HRTF Clustering and Synthesis with 3-D Sound

• Main Authors: C. C. Chuang, 莊志強
• Other Authors: S. F. Hsieh
• Format: Others
• Language: en_US
• Published: 1995
• Online Access: http://ndltd.ncl.edu.tw/handle/74467523991558310906

碩士 === 國立交通大學 === 電信研究所 === 83 === HRTFs are the transfer functions from 3-D positions to both ears. A total of 710 HRTFs, measured from the dummy head at the MIT Media Lab, constituted the data set to be processed. This thesis describes clustering and PCA approaches to simplify HRTF clustering aims to choose some most significant HRTFs among the whole data set. We propose to use cepstrum clustering, as opposed to uniform to achieve lower mismatch error. The essence of the PCA algorithm is to search for some basic so that the attributes of HRTFs are the combination of these. We will discuss and compare three PCA algorithms (LM-PCA, M-PCA, and the QR method with pivoting. Another merit of the PCA is to interpolate new HRTFs which are excluded in the data set. Listening tests of a moving sound source are also made to justify these algorithms.