showing 3 related works from this author
Real-time Sound Source Localization on an Embedded GPU Using a Spherical Microphone Array
2015
Abstract Spherical microphone arrays are becoming increasingly important in acoustic signal processing systems for their applications in sound field analysis, beamforming, spatial audio, etc. The positioning of target and interfering sound sources is a crucial step in many of the above applications. Therefore, 3D sound source localization is a highly relevant topic in the acoustic signal processing field. However, spherical microphone arrays are usually composed of many microphones and running signal processing localization methods in real time is an important issue. Some works have already shown the potential of Graphic Processing Units (GPUs) for developing high-end real-time signal proce…
On the Use of a GPU-Accelerated Mobile Device Processor for Sound Source Localization
2017
Abstract The growing interest to incorporate new features into mobile devices has increased the number of signal processing applications running over processors designed for mobile computing. A challenging signal processing field is acoustic source localization, which is attractive for applications such as automatic camera steering systems, human-machine interfaces, video gaming or audio surveillance. In this context, the emergence of systems-on-chip (SoC) that contain a small graphics accelerator (or GPU), contributes a notable increment of the computational capacity while partially retaining the appealing low-power consumption of embedded systems. This is the case, for example, of the Sam…
Compression and load balancing for efficient sparse matrix-vector product on multicore processors and graphics processing units
2021
We contribute to the optimization of the sparse matrix-vector product by introducing a variant of the coordinate sparse matrix format that balances the workload distribution and compresses both the indexing arrays and the numerical information. Our approach is multi-platform, in the sense that the realizations for (general-purpose) multicore processors as well as graphics accelerators (GPUs) are built upon common principles, but differ in the implementation details, which are adapted to avoid thread divergence in the GPU case or maximize compression element-wise (i.e., for each matrix entry) for multicore architectures. Our evaluation on the two last generations of NVIDIA GPUs as well as In…