手法を比較

	メル周波数ケプストラム係数（MFCC）	頭部伝達関数	独立ベクトル解析
分野	応用物理学	応用物理学	応用物理学
系統	Process / pipeline	Process / pipeline	Process / pipeline
提唱年	1980	1989	2007
提唱者	Steven Davis, Paul Mermelstein	Fredrik Wightman, Doris Kistler	Tae-Won Lee, Mark Lewicki, Terrence Sejnowski
種類	Audio feature extraction algorithm	Frequency-dependent spatial filtering function	Multivariate matrix decomposition algorithm
原典	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.	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.
別名	mel-cepstral features, MFCC features, mel-frequency features	HRTF, spatial hearing, binaural filter	IVA, multivariate ICA, vector blind source separation
関連	3	3	3
概要	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.	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.