Approximate convolution using partitioned truncated singular value decomposition filtering for binaural rendering
| Autor: | Joshua Atkins, Adam Strauss, Chen Zhang |
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| Rok vydání: | 2013 |
| Předmět: |
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Theoretical computer science Acoustics and Ultrasonics Computer science Filter (signal processing) Impulse (physics) Convolution Rendering (computer graphics) Singular value Matrix (mathematics) Arts and Humanities (miscellaneous) Singular value decomposition business Binaural recording Algorithm Headphones |
| Zdroj: | Proceedings of Meetings on Acoustics. |
| ISSN: | 1939-800X |
| DOI: | 10.1121/1.4800867 |
| Popis: | In conventional binaural rendering a pair of head-related impulse responses (HRIR), measured from source direction to left and right ears, is convolved with a source signal to create the impression of a virtual 3D sound source when played on headphones. It is well known that using HRIRs measured in a real room, which includes a natural reverberant decay, increases the externalization and realism of the simulation. However, the HRIR filter length in even a small room can be many thousands of taps leading to computational complexity issues in real world implementations. We propose a new method, partitioned truncated singular value decomposition (PTSVD) filtering, for approximating the convolution by partitioning the HRIR filters in time, performing a singular value decomposition on the matrix of filter partitions, and choosing the M singular-vectors corresponding to the M largest singular values to reconstruct the HRIR filters. We will show how this can be implemented in an efficient filter-bank type structure with M tapped delay lines for real-time application. We also show how improvements to the method, such as modeling the direct path HRIR separately can lead to improved rendering at minimal computational load. |
| Databáze: | OpenAIRE |
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