Efficient range extrapolation of head-related impulse responses by wave field synthesis techniques

Spors, Sascha; Ahrens, Jens

doi:10.1109/icassp.2011.5946325

Cited by 8 publications

(6 citation statements)

References 7 publications

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“…1 To account for the distance-dependence of HRTFs in the near-field, approaches have been proposed to estimate near-field HRTFs from measurements taken at a single distance. 2,[6][7][8][9] Recently, HRTF databases have been published containing measurements taken at various distances in the near-field. [10][11][12] However, there is a lack of algorithms and tools to make use of such databases.…”

Section: Introductionmentioning

confidence: 99%

Head-related transfer function interpolation in azimuth, elevation, and distance

Gamper

2013

The Journal of the Acoustical Society of America

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Although distance-dependent head-related transfer function (HRTF) databases provide interesting possibilities, e.g., for rendering virtual sounds in the near-field, there is a lack of algorithms and tools to make use of them. Here, a framework is proposed for interpolating HRTF measurements in 3-D (i.e., azimuth, elevation, and distance) using tetrahedral interpolation with barycentric weights. For interpolation, a tetrahedral mesh is generated via Delaunay triangulation and searched via an adjacency walk, making the framework robust with respect to irregularly positioned HRTF measurements and computationally efficient. An objective evaluation of the proposed framework indicates good accordance between measured and interpolated nearfield HRTFs.

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Section: Introductionmentioning

confidence: 99%

Head-related transfer function interpolation in azimuth, elevation, and distance

Gamper

2013

The Journal of the Acoustical Society of America

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“…Tailored HRTFs are usually obtained either through measurement and extrapolation [17] or via numerical simulation [14]. There are multiple public samples of HRTFs or HRIRs [5,2].…”

Section: Spatial Soundmentioning

confidence: 99%

Evaluating the Potential of Low Power Systems for Headphone-based Spatial Audio Applications

Belloch

González

Mayo

et al. 2015

Procedia Computer Science

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Embedded architectures have been traditionally designed tailored to perform a dedicated (specialized) function, and in general feature a limited amount of processing resources as well as exhibit very low power consumption. In this line, the recent introduction of systems-on-chip (SoC) composed of low power multicore processors, combined with a small graphics accelerator (or GPU), presents a notable increment of the computational capacity while partially retaining the appealing low power consumption of embedded systems. This paper analyzes the potential of these new hardware systems to accelerate applications that integrate spatial information into an immersive audiovisual virtual environment or into video games. Concretely, our work discusses the implementation and performance evaluation of a headphone-based spatial audio application on the Jetson TK1 development kit, a board equipped with a SoC comprising a quad-core ARM processor and an NVIDIA "Kepler" GPU. Our implementations exploit the hardware parallelism of both types of architectures by carefully adapting the underlying numerical computations. The experimental results show that the accelerated application is able to move up to 300 sound sources simultaneously in real time on this platform.

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“…Individualized HRTFs are traditionally obtained either through measurements and extrapolations [5] or numerical simulation [6]. There exists a big quantity of HRTFs data-bases on internet.…”

Section: Head Related Transfer Functions (Hrtfs)mentioning

confidence: 99%

Headphone-based Spatial Sound with a GPU Accelerator

Belloch

Ferrer

González

et al. 2012

Procedia Computer Science

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Multichannel acoustic signal processing has undergone major development in recent years. The incorporation of spatial information into an immersive audiovisual virtual environment or into video games provides better sense of "presence" to applications. Spatial sound consists in reproducing audio signals with spatial cues (spatial information embedded in the sound) through headphones. This spatial information allows the listener to identify the virtual positions of the sources corresponding to different sounds. Headphone-based spatial sound is obtained by filtering different sound sources through special filters called Head Related Transfer Functions (HRTFs) prior to render them through headphones. Efficient computation plays an important role when the number of sources to be managed is high. This situation increases the number of filtering operations, requiring high computing capacity specially when the virtual sources are moving. Graphics Processing Units (GPUs) are high parallel programmable co-processors that provide massive computation when the needed operations are properly parallelized. This paper discusses the design, the implementation and the performance of a headphone-based spatial audio application whose processing is totally carried out on a GPU. This application is able to interact with the listener who can select and change the location of the sound sources in real-time. This work analyzes also specific computational aspects inside the CUDA environment in order to successfully exploit GPU resources. Results show that the proposed application is able to move up to 2500 sources simultaneously, while leaving free CPU resources for other tasks. This work emphasizes the importance of analyzing all CUDA aspects, since they can influence drastically the performance.

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Efficient range extrapolation of head-related impulse responses by wave field synthesis techniques

Cited by 8 publications

References 7 publications

Head-related transfer function interpolation in azimuth, elevation, and distance

Head-related transfer function interpolation in azimuth, elevation, and distance

Evaluating the Potential of Low Power Systems for Headphone-based Spatial Audio Applications

Headphone-based Spatial Sound with a GPU Accelerator

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