Spherical harmonic representation of rectangular domain sound fields
نویسندگان
چکیده
منابع مشابه
ACOUSTICS2008/265 Surround Sound Echo Cancellation in the Spherical Harmonic Domain
The problem of creating a multiuser hands-free immersive telecommunications environment poses many challenges for acoustic signal processing. The most pressing is the creation of a fast multi-channel acoustic echo canceller (MCAEC) to eliminate acoustic feedback created in the speaker-microphone loop. Traditional multi-channel adaptive algorithms for echo cancellation are not fast enough to wor...
متن کاملRotation Invariant Spherical Harmonic Representation of 3D Shape Descriptors
One of the challenges in 3D shape matching arises from the fact that in many applications, models should be considered to be the same if they differ by a rotation. Consequently, when comparing two models, a similarity metric implicitly provides the measure of similarity at the optimal alignment. Explicitly solving for the optimal alignment is usually impractical. So, two general methods have be...
متن کاملReconstruction of sound fields with a spherical microphone array
Spherical microphone arrays are very well suited for sound field measurements in enclosures or interior spaces, and generally in acoustic environments where sound waves impinge on the array from multiple directions. Because of their directional properties, they make it possible to resolve sound waves traveling in any direction. In particular, rigid sphere microphone arrays are robust, and have ...
متن کاملMulti-scale Voxel-Based Morphometry Via Weighted Spherical Harmonic Representation
Although the voxel-based morphometry (VBM) has been widely used in quantifying the amount of gray matter of the human brain, the optimal amount of registration that should be used in VBM has not been addressed. In this paper, we present a novel multi-scale VBM using the weighted spherical harmonic (SPHARM) representation to address the issue. The weighted-SPHARM provides the explicit smooth fun...
متن کاملTensor-based Cortical Morphometry via Weighted Spherical Harmonic Representation
We present a new tensor-based morphometric framework that quantifies cortical shape variations using the local area element. The local area element is obtained from the Riemannian metric tensors, which are, in turn, obtained from the smooth functional parametrization of a triangle mesh. For the smooth parametrization, we have developed a novel weighted spherical harmonic (SPHARM) representation...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Acoustical Science and Technology
سال: 2020
ISSN: 1346-3969,1347-5177
DOI: 10.1250/ast.41.451