Abstract
The extended use of mobile multimedia devices in applications like gaming, 3D video and audio reproduction, immersive teleconferencing, or virtual and augmented reality, is demanding efficient algorithms and methodologies. All these applications require real-time spatial audio engines with the capability of dealing with intensive signal processing operations while facing a number of constraints related to computational cost, latency and energy consumption. Most mobile multimedia devices include a Graphics Processing Unit (GPU) that is primarily used to accelerate video processing tasks, providing high computational capabilities due to its inherent parallel architecture. This paper describes a scalable parallel implementation of a real-time binaural audio engine for GPU-equipped mobile devices. The engine is based on a set of head-related transfer functions (HRTFs) modelled with a parametric parallel structure, allowing efficient synthesis and interpolation while reducing the size required for HRTF data storage. Several strategies to optimize the GPU implementation are evaluated over a well-known kind of processor present in a wide range of mobile devices. In this context, we analyze both the energy consumption and real-time capabilities of the system by exploring different GPU and CPU configuration alternatives. Moreover, the implementation has been conducted using the OpenCL framework, guarantying the portability of the code.
Highlights
Applications using spatial audio rendering are gaining popularity, mainly due to the widespread use of multimedia-capable mobile devices such as phones and tablets
This work proposes an efficient implementation of a spatial audio engine based on a parametric parallel filter bank for binaural synthesis
The limitations of conventional seriesto-parallel conversion using partial-fraction-expansion have been presented to motivate the use of the developed parallel approach, which was designed to use efficiently the parallel computation resources found in modern mobile multimedia devices
Summary
Applications using spatial audio rendering are gaining popularity, mainly due to the widespread use of multimedia-capable mobile devices such as phones and tablets. The efficient implementation of low-order models on parallel architectures like GPUs can have a considerable impact on the final computational cost and energy consumption. Instead of defining and storing the filter coefficients of each SOS, physical parameters (frequency, gains, and quality factor Q) are used This parametric approach, as it will be seen, allows a simple interpolation method for obtaining the HRTFs at azimuth and elevation angles that have not been modeled, and it will need a lower database size for storing the complete HRTF set. Results show that by using the low power-consuming kind of CPU core (Cortex-A7) as host and lowering the frequency of the CPU and GPU cores, we can greatly reduce the energy consumption, while still being able to process up to 16 sound sources in real-time.
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