Linganisha mbinu

	Ambisonics	Kazi ya uhamishaji wa kichwa (HRTF)	MFCC (Mel-Frequency Cepstral Coefficients)
Nyanja	Fizikia Tumizi	Fizikia Tumizi	Fizikia Tumizi
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Mwaka wa asili	1973	1989	1980
Mwanzilishi	Michael Gerzon	Fredrik Wightman, Doris Kistler	Steven Davis, Paul Mermelstein
Aina	Spatial audio encoding and reproduction technique	Frequency-dependent spatial filtering function	Audio feature extraction algorithm
Chanzo asilia	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.	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.
Majina mbadala	spatial audio, B-format, ambisonic recording	HRTF, spatial hearing, binaural filter	mel-cepstral features, MFCC features, mel-frequency features
Zinazohusiana	3	3	3
Muhtasari	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.	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.