השוואת שיטות

	פונקציית העברה הקשורה לראש	אמביסוניקס	ניתוח וקטורים בלתי תלויים
תחום	פיזיקה יישומית	פיזיקה יישומית	פיזיקה יישומית
משפחה	Process / pipeline	Process / pipeline	Process / pipeline
שנת המקור	1989	1973	2007
הוגה השיטה	Fredrik Wightman, Doris Kistler	Michael Gerzon	Tae-Won Lee, Mark Lewicki, Terrence Sejnowski
סוג	Frequency-dependent spatial filtering function	Spatial audio encoding and reproduction technique	Multivariate matrix decomposition algorithm
מקור מכונן	Wightman, F. L., & Kistler, D. J. (1989). Headphone simulation of free-field listening. I: Stimulus synthesis. The Journal of the Acoustical Society of America, 85(2), 858-867.	Gerzon, M. A. (1973). Periphony: with-height sound reproduction. Journal of the Audio Engineering Society, 21(1), 2-10.	Lee, T. W., Lewicki, M. S., & Sejnowski, T. J. (2007). Independent Component Analysis for Source Localization in Biomedical Signals. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process., pp. 97-100.
כינויים	HRTF, spatial hearing, binaural filter	spatial audio, B-format, ambisonic recording	IVA, multivariate ICA, vector blind source separation
קשורות	3	3	3
תקציר	The Head-Related Transfer Function (HRTF) describes how the human head, ears, and torso filter sound from different directions. HRTFs capture the acoustical changes that occur as sound travels around the head to reach each ear, enabling the perception of sound location in 3D space. Measured or modeled HRTFs are essential for creating convincing 3D audio through headphones in virtual reality, spatial games, and immersive audio applications.	Ambisonics is a full-sphere spatial audio encoding and reproduction technique that captures and reproduces three-dimensional sound fields. Developed by Michael Gerzon in the 1970s, it uses spherical harmonics to represent sound at all directions around a central point. Unlike surround systems that use discrete channels, Ambisonics provides a format-agnostic spatial representation that can be rotated, translated, and rendered to any speaker configuration.	Independent Vector Analysis (IVA) is a multivariate extension of Independent Component Analysis that jointly separates multiple datasets while maintaining dependencies within each dataset. Developed by Lee, Lewicki, and Sejnowski in the 2000s, IVA is used for blind source separation in multi-channel audio, brain imaging, and signal processing. It exploits both the independence between sources and correlations within frequency bands or time-frequency structures.