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RESEARCH PRODUCT

A Parallel Approach to HRTF Approximation and Interpolation Based on a Parametric Filter Model

Maximo CobosBalázs BankJose A. BellochGerman Ramos

subject

Computer scienceparallel filters02 engineering and technologySolid modelingbinaural synthesisTransfer functionTECNOLOGIA ELECTRONICA030507 speech-language pathology & audiology03 medical and health sciencesgraphic processing unit (GPU)0202 electrical engineering electronic engineering information engineeringCIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIALhead-related transfer function (HRTF) modelingComputer visionElectrical and Electronic EngineeringAdaptation (computer science)Parametric statisticsbusiness.industryApplied MathematicsTeleconferenceBinaural synthesis020206 networking & telecommunicationsFilter (signal processing)interpolationInterpolationGraphic processing unit (GPU)Signal ProcessingHead-related transfer function (HRTF) modelingParallel filtersArtificial intelligence0305 other medical sciencebusinessAlgorithmInterpolation

description

[EN] Spatial audio-rendering techniques using head-related transfer functions (HRTFs) are currently used in many different contexts such as immersive teleconferencing systems, gaming, or 3-D audio reproduction. Since all these applications usually involve real-time constraints, efficient processing structures for HRTF modeling and interpolation are necessary for providing real-time binaural audio solutions. This letter presents a parametric parallel model that allows us to perform HRTF filtering and interpolation efficiently from an input HRTF dataset. The resulting model, which is an adaptation from a recently proposed modeling technique, not only reduces the size of HRTF datasets significantly, but also allows for simplified interpolation and real-time computation over parallel processors. In order to discuss the suitability of this new model, an implementation over a graphic processing unit is presented.

yearjournalcountryeditionlanguage
2017-10-01
10.1109/lsp.2017.2741724https://doi.org/10.1109/lsp.2017.2741724