Módszerek összehasonlítása

	Ambisonics	Fej-függő átviteli függvény	Független Vektor Analízis
Tudományterület	Alkalmazott fizika	Alkalmazott fizika	Alkalmazott fizika
Módszercsalád	Process / pipeline	Process / pipeline	Process / pipeline
Keletkezés éve	1973	1989	2007
Megalkotó	Michael Gerzon	Fredrik Wightman, Doris Kistler	Tae-Won Lee, Mark Lewicki, Terrence Sejnowski
Típus	Spatial audio encoding and reproduction technique	Frequency-dependent spatial filtering function	Multivariate matrix decomposition algorithm
Alapmű	Gerzon, M. A. (1973). Periphony: with-height sound reproduction. Journal of the Audio Engineering Society, 21(1), 2-10.	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.	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.
Alternatív nevek	spatial audio, B-format, ambisonic recording	HRTF, spatial hearing, binaural filter	IVA, multivariate ICA, vector blind source separation
Kapcsolódó	3	3	3
Összefoglaló	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.	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.	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.