A spatial feature extraction and regularization model for virtual auditory display

Authors:
Jiashu Chen;Barry D. Van Veen;Kurt E. Hecox
Affiliations:
University of Wisconsin-Madison, Madison, WI;University of Wisconsin-Madison, Madison, WI;University of Wisconsin-Madison, Madison, WI
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: plenary, special, audio, underwater acoustics, VLSI, neural networks - Volume I
Year:
1993

Citing 2
Cited 1

System identification: theory for the user

System identification: theory for the user
Orthogonal Transforms for Digital Signal Processing

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On the feasibility of real-time phone-to-phone 3D localization

Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems

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Abstract

In this paper a spatial feature extraction and regularization model is developed to represent free-field-to-eardrum transfer functions (FETFs). A Karhunen-Loève expansion is used to derive a low dimensional eigen-transfer function (EF) subspace for the measured FETFs. The coordinates of each FETF in the subspace are determined by projecting all measured FETFs onto the EFs. These coordinates represent samples of the FETFs' spatial features. Functional representations of the spatial features, termed Spatial Characteristic Functions (SCFs), are obtained by applying a thin-plate generalized spline smoothing model to regularize the samples. A functional representation for the FETF is thus obtained by linearly combining the EFs with the SCFs. Typical errors between the measured and modeled FETFs for a KEMAR are on the order of hundredth of 1%.