On the performance of multi-GPU-based expert systems for acoustic localization involving massive microphone array

Sound source localization is an important topic in expert systems involving microphone arrays, such as automatic camera steering systems, human-machine interaction, video gaming or audio surveillance. The Steered Response Power with Phase Transform (SRP-PHAT) algorithm is a well-known approach for sound source localization due to its robust performance in noisy and reverberant environments. This algorithm analyzes the sound power captured by an acoustic beamformer on a defined spatial grid, estimating the source location as the point that maximizes the output power. Since localization accuracy can be improved by using high-resolution spatial grids and a high number of microphones, accurate acoustic localization systems require high computational power. Graphics Processing Units (GPUs) are highly parallel programmable co-processors that provide massive computation when the needed operations are properly parallelized. Emerging GPUs offer multiple parallelism levels; however, properly managing their computational resources becomes a very challenging task. In fact, management issues become even more difficult when multiple GPUs are involved, adding one more level of parallelism. In this paper, the performance of an acoustic source localization system using distributed microphones is analyzed over a massive multichannel processing framework in a multi-GPU system. The paper evaluates and points out the influence that the number of microphones and the available computational resources have in the overall system performance. Several acoustic environments are considered to show the impact that noise and reverberation have in the localization accuracy and how the use of massive microphone systems combined with parallelized GPU algorithms can help to mitigate substantially adverse acoustic effects. In this context, the proposed implementation is able to work in real time with high-resolution spatial grids and using up to 48 microphones. These results confirm the advantages of suitable GPU architectures in the development of real-time massive acoustic signal processing systems.This work has been partially funded by the Spanish Ministerio de Economia y Competitividad (TEC2009-13741, TEC2012-38142-C04-01, and TEC2012-37945-C02-02), Generalitat Valenciana PROMETEO 2009/2013, and Universitat Politecnica de Valencia through Programa de Apoyo a la Investigacion y Desarrollo (PAID-05-11 and PAID-05-12).Belloch Rodríguez, JA.; Gonzalez, A.; Vidal Maciá, AM.; Cobos Serrano, M. (2015). On the performance of multi-GPU-based expert systems for acoustic localization involving massive microphone array. Expert Systems with Applications. 42(13):5607-5620. https://doi.org/10.1016/j.eswa.2015.02.056S56075620421

Accelerating the SRP-PHAT algorithm on multi and many-core platforms using OpenCL

[EN] The Steered Response Power with Phase Transform (SRP-PHAT) algorithm is a well-known method for sound source localization due to its robust performance in noisy and reverberant environments. This algorithm is used in a large number of acoustic applications such as automatic camera steering systems, human-machine interaction, video gaming and audio surveillance. SPR-PHAT implementations require to handle a high number of signals coming from a microphone array and a huge search grid that influences the localization accuracy of the system. In this context, high performance in the localization process can only be achieved by using massively parallel computational resources. Different types of multi-core machines based either on multiple CPUs or on GPUs are commonly employed in diverse fields of science for accelerating a number of applications, mainly using OpenMP and CUDA as programming frameworks, respectively. This implies the development of multiple source codes which limits the portability and application possibilities. On the contrary, OpenCL has emerged as an open standard for parallel programming that is nowadays supported by a wide range of architectures. In this work, we evaluate an OpenCL-based implementations of the SRP-PHAT algorithm in two state-of-the-art CPU and GPU platforms. Results demonstrate that OpenCL achieves close-to-CUDA performance in GPU (considered as upper bound) and outperforms in most of the CPU configurations based on OpenMP.This work has been supported by the postdoctoral fellowship from Generalitat Valenciana APOSTD/2016/069, the Spanish Government through TIN2014-53495-R, TIN2015-65277-R and BIA2016-76957-C3-1-R, and the Universidad Jaume I Project UJI-B2016-20.Badía Contelles, JM.; Belloch Rodríguez, JA.; Cobos Serrano, M.; Igual Peña, FD.; Quintana-Ortí, ES. (2019). Accelerating the SRP-PHAT algorithm on multi and many-core platforms using OpenCL. The Journal of Supercomputing. 75(3):1284-1297. https://doi.org/10.1007/s11227-018-2422-6S12841297753Brandstein M, Ward D (eds) (2001) Microphone arrays. Springer, BerlinKnapp CH, Carter GC (1976) The generalized correlation method for estimation of time delay. Trans Acoust Speech Signal Process 24:320–327Cobos M, Antonacci F, Alexandridis A, Mouchtaris A, Lee B (2017) A survey of sound source localization methods in wireless acoustic sensor networks. Wirel Commun Mobile Comput 2017, article ID 3956282DiBiase JH (2000) A high accuracy, low-latency technique for talker localization in reverberant environments using microphone arrays. Ph.D. dissertation, Brown University, ProvidenceLee CH (2017) Location-aware speakers for the virtual reality environments. IEEE Access 5:2636–2640Altera Corporation (2013) Implementing FPGA design with the OpenCL standard. https://www.altera.com/en_US/pdfs/literature/wp/wp-01173-opencl.pdf . Accessed 21 May 2018Savioja L, Välimäki V, Smith JO (2011) Audio signal processing using graphics processing units. J Audio Eng Soc 59(1–2):3–19Belloch JA, Gonzalez A, Martínez-Zaldívar FJ, Vidal AM (2011) Real-time massive convolution for audio applications on GPU. J Supercomput 58(3):449–457Belloch JA, Gonzalez A, Quintana-Ortí ES, Ferrer M, Välimäki V (2017) GPU-based dynamic wave field synthesis using fractional delay filters and room compensation. IEEE/ACM Trans Audio Speech Lang Process 25(2):435–447Peruffo Minotto V, Rosito Jung C, Gonzaga da Silveira L, Lee B (2013) GPU-based approaches for real-time sound source localization using the SRP-PHAT algorithm. Int J High Perform Comput Appl 27(3):291–306Belloch JA, Gonzalez A, Vidal AM, Cobos M (2015) On the performance of multi-gpu-based expert systems for acoustic localization involving massive microphone arrays. Expert Syst Appl 42(13):5607–5620Seewald LC, Gonzaga L, Veronez MR, Minotto VP, Jung CR (2014) Combining srp-phat and two kinects for 3d sound source localization. Expert Syst Appl 41(16):0957–4174Theodoropoulos D, Kuzmanov G, Gaydadjiev G (2011) Multi-core platforms for beamforming and wave field synthesis. IEEE Trans Multimedia 3(2):235–245Belloch JA, Badia MJ, Igual FD, Quintana-Ortí E, Cobos M (2017) Evaluating sound source localization on multi and many-core platform. In: Proceedings of the 17th International Conference on Computational and Mathematical Methods in Science and Engineering, vol 1. Rota, pp 279–286Cobos M, Marti A, Lopez JJ (2011) A modified SRP-PHAT functional for robust real-time sound source localization with scalable spatial sampling. IEEE Signal Process Lett 18(1):71–74Marti A, Cobos M, Lopez JJ (2013) A steered response power iterative method for high-accuracy acoustic source location. J Acoust Soc Am 134(4):2627–2630Frigo M, Johnson SG (2005) The design and implementation of FFTW3. Proc IEEE 93(2):216–231 (special issue on “Program generation, optimization, and platform adaptation”)NVIDIA cuFFT library user’s guide (2018). https://docs.nvidia.com/cuda/pdf/CUFFT_Library.pdf . Accessed 21 May 2018OpenCL fast Fourier transforms. http://clmathlibraries.github.io/clFFT . Accessed 21 May 2018Scarpino M (2012) OpenCL in action: how to accelerate graphics and computation. Mannin

Practical considerations for acoustic source localization in the IoT era: Platforms, energy efficiency, and performance

The rapid development of the Internet of Things (IoT) has posed important changes in the way emerging acoustic signal processing applications are conceived. While traditional acoustic processing applications have been developed taking into account high-throughput computing platforms equipped with expensive multichannel audio interfaces, the IoT paradigm is demanding the use of more flexible and energy-efficient systems. In this context, algorithms for source localization and ranging in wireless acoustic sensor networks can be considered an enabling technology for many IoT-based environments, including security, industrial, and health-care applications. This paper is aimed at evaluating important aspects dealing with the practical deployment of IoT systems for acoustic source localization. Recent systems-on-chip composed of low-power multicore processors, combined with a small graphics accelerator (or GPU), yield a notable increment of the computational capacity needed in intensive signal processing algorithms while partially retaining the appealing low power consumption of embedded systems. Different algorithms and implementations over several state-of-the-art platforms are discussed, analyzing important aspects, such as the tradeoffs between performance, energy efficiency, and exploitation of parallelism by taking into account real-time constraintsThis work was supported in part by the Post-Doctoral Fellowship from Generalitat Valenciana under Grant APOSTD/2016/069, in part by the Spanish Government under Grant TIN2014-53495-R, Grant TIN2015-65277-R, and Grant BIA2016-76957-C3-1-R, and in part by the Universidad Jaume I under Project UJI-B2016-20.Publicad

Design and Implementation of Acoustic Source Localization on a Low-Cost IoT Edge Platform

The implementation of algorithms for acoustic source localization on edge platforms for the Internet of Things (IoT) is gaining momentum. Applications based on acoustic monitoring can greatly benefit from efficient implementations of such algorithms, enabling novel services for smart homes and buildings or ambient-assisted living. In this context, this brief proposes extreme low-cost sound source localization system composed of two microphones and the low power microcontroller module ESP32. A Direction-Of-Arrival (DOA) algorithm has been implemented taking into account the specific features of this board, showing excellent performance despite the memory constraints imposed by the platform. We have also adapted off-the-shelf lowcost microphone boards to the input requirements of the ESP32 Analog-to-Digital Converter. The processing has been optimized by leveraging in parallel both cores of the microcontroller to capture and process the audio in real time. Our experiments expose that we can perform real-time localization, with a processing time below 3.3 ms.This work was supported in part by the Spanish Government under Grant TIN2017-82972-R, Grant ESP2015-68245-C4-1-P, and Grant RTI2018-097045-B-C21, and in part by the Valencian Regional Government under Grant PROMETEO/2019/109

Contextual Beamforming: Exploiting Location and AI for Enhanced Wireless Telecommunication Performance

The pervasive nature of wireless telecommunication has made it the foundation for mainstream technologies like automation, smart vehicles, virtual reality, and unmanned aerial vehicles. As these technologies experience widespread adoption in our daily lives, ensuring the reliable performance of cellular networks in mobile scenarios has become a paramount challenge. Beamforming, an integral component of modern mobile networks, enables spatial selectivity and improves network quality. However, many beamforming techniques are iterative, introducing unwanted latency to the system. In recent times, there has been a growing interest in leveraging mobile users' location information to expedite beamforming processes. This paper explores the concept of contextual beamforming, discussing its advantages, disadvantages and implications. Notably, the study presents an impressive 53% improvement in signal-to-noise ratio (SNR) by implementing the adaptive beamforming (MRT) algorithm compared to scenarios without beamforming. It further elucidates how MRT contributes to contextual beamforming. The importance of localization in implementing contextual beamforming is also examined. Additionally, the paper delves into the use of artificial intelligence schemes, including machine learning and deep learning, in implementing contextual beamforming techniques that leverage user location information. Based on the comprehensive review, the results suggest that the combination of MRT and Zero forcing (ZF) techniques, alongside deep neural networks (DNN) employing Bayesian Optimization (BO), represents the most promising approach for contextual beamforming. Furthermore, the study discusses the future potential of programmable switches, such as Tofino, in enabling location-aware beamforming

GPU Acceleration of a Non-Standard Finite Element Mesh Truncation Technique for Electromagnetics

The emergence of General Purpose Graphics Processing Units (GPGPUs) provides new opportunities to accelerate applications involving a large number of regular computations. However, properly leveraging the computational resources of graphical processors is a very challenging task. In this paper, we use this kind of device to parallelize FE-IIEE (Finite Element-Iterative Integral Equation Evaluation), a non-standard finite element mesh truncation technique introduced by two of the authors. This application is computationally very demanding due to the amount, size and complexity of the data involved in the procedure. Besides, an efficient implementation becomes even more difficult if the parallelization has to maintain the complex workflow of the original code. The proposed implementation using CUDA applies different optimization techniques to improve performance. These include leveraging the fastest memories of the GPU and increasing the granularity of the computations to reduce the impact of memory access. We have applied our parallel algorithm to two real radiation and scattering problems demonstrating speedups higher than 140 on a state-of-the-art GPU.This work was supported in part by the Spanish Government under Grant TEC2016-80386-P, Grant TIN2017-82972-R, and Grant ESP2015-68245-C4-1-P, and in part by the Valencian Regional Government under Grant PROMETEO/2019/109