Порівняння методів

	Перетворення передавальної функції голови (HRTF)	Амбісоніка	Мел-частотні кепстральні коефіцієнти (MFCC)
Галузь	Прикладна фізика	Прикладна фізика	Прикладна фізика
Родина	Process / pipeline	Process / pipeline	Process / pipeline
Рік появи	1989	1973	1980
Автор методу	Fredrik Wightman, Doris Kistler	Michael Gerzon	Steven Davis, Paul Mermelstein
Тип	Frequency-dependent spatial filtering function	Spatial audio encoding and reproduction technique	Audio feature extraction algorithm
Основоположне джерело	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.	Davis, S., & Mermelstein, P. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28(4), 357-366.
Інші назви	HRTF, spatial hearing, binaural filter	spatial audio, B-format, ambisonic recording	mel-cepstral features, MFCC features, mel-frequency features
Пов'язані	3	3	3
Підсумок	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.	Mel-Frequency Cepstral Coefficients (MFCCs) are a compact representation of audio features that mimic human auditory perception. Introduced by Davis and Mermelstein in 1980, MFCCs are the de facto feature extraction method for speech recognition and environmental sound analysis. They compress the frequency information of audio signals into a small set of coefficients that capture phonetic content while discarding irrelevant details.