以HRTF群組及合成作3D音效處理

Abstract

空間到雙耳的特徵響應稱之為 HRTF.一共有 710 院媒體實驗室 處理應 用.本篇論文描述了群組法和主軸分析法,兩種方式來簡化 HRTFs.群組法 是以量化的觀點為基礎來找出代表性的特徵響應,本篇提供了 cepstrum 群組法,有別於一般的均勻群組法.主軸分析法包括了 LM-PCA,還有 QR分 析法的精神是找出特徵響應的基本函數,藉著基本函數 釭滲S徵響應並且 合成新的特徵響應.除了結果分析之外我們並會以移動音源來作為聽覺測 試上的比較. 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.