CABE: A Cloud-Based Acoustic Beamforming Emulator for FPGA-Based Sound Source Localization

Segers, Laurent; Vandendriessche, Jurgen; Vandervelden, Thibaut; Lapauw, Benjamin Johan; Silva, Bruno da; Braeken, An; Touhafi, Abdellah

doi:10.3390/s19183906

Cited by 2 publications

(4 citation statements)

References 37 publications

(56 reference statements)

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“…Both the geometry of the microphone array and the signal processing of the acoustic signal have a direct impact on the acoustic camera response. Evaluation tools such as the Cloud-based Acoustic Beamforming Emulator (CABE) enable an early evaluation of an array’s geometries and the frequency response of the acoustic cameras [ 34 ].…”

Section: Acoustic Map Imaging Datasetmentioning

confidence: 99%

“…In order to have a representative dataset, the same architecture as in [ 34 ] was used. The order of the filters and the decimation factor can be found in Table 1 .…”

Section: Acoustic Map Imaging Datasetmentioning

confidence: 99%

“…Doing so will omit everything that comes before the filtering stage and replace it by the PDM signals from the real life recording. In order to have a representative dataset, the same architecture as in [35] is used. The order of the filters and the decimation factor can be found in Table 1.…”

Section: Generation Of Acoustic Datasetsmentioning

confidence: 99%

“…This section first introduces the characteristics of the target acoustic camera [6] and the imaging emulator [35]. The procedure for capturing multiple scale acoustic map images, the analysis of the dataset, and the applied standardization procedure are also described.…”

Section: Acoustic Map Imaging Datasetmentioning

confidence: 99%

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XCycles Backprojection Acoustic Super-Resolution

Almasri

Vandendriessche

Segers

et al. 2021

Sensors

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The computer vision community has paid much attention to the development of visible image super-resolution (SR) using deep neural networks (DNNs) and has achieved impressive results. The advancement of non-visible light sensors, such as acoustic imaging sensors, has attracted much attention, as they allow people to visualize the intensity of sound waves beyond the visible spectrum. However, because of the limitations imposed on acquiring acoustic data, new methods for improving the resolution of the acoustic images are necessary. At this time, there is no acoustic imaging dataset designed for the SR problem. This work proposed a novel backprojection model architecture for the acoustic image super-resolution problem, together with Acoustic Map Imaging VUB-ULB Dataset (AMIVU). The dataset provides large simulated and real captured images at different resolutions. The proposed XCycles BackProjection model (XCBP), in contrast to the feedforward model approach, fully uses the iterative correction procedure in each cycle to reconstruct the residual error correction for the encoded features in both low- and high-resolution space. The proposed approach was evaluated on the dataset and showed high outperformance compared to the classical interpolation operators and to the recent feedforward state-of-the-art models. It also contributed to a drastically reduced sub-sampling error produced during the data acquisition.

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Section: Acoustic Map Imaging Datasetmentioning

confidence: 99%

“…In order to have a representative dataset, the same architecture as in [ 34 ] was used. The order of the filters and the decimation factor can be found in Table 1 .…”

Section: Acoustic Map Imaging Datasetmentioning

confidence: 99%

Section: Generation Of Acoustic Datasetsmentioning

confidence: 99%

Section: Acoustic Map Imaging Datasetmentioning

confidence: 99%

See 2 more Smart Citations

XCycles Backprojection Acoustic Super-Resolution

Almasri

Vandendriessche

Segers

et al. 2021

Sensors

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M3-AC: A Multi-Mode Multithread SoC FPGA Based Acoustic Camera

et al. 2021

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Acoustic cameras allow the visualization of sound sources using microphone arrays and beamforming techniques. The required computational power increases with the number of microphones in the array, the acoustic images resolution, and in particular, when targeting real-time. Such a constraint limits the use of acoustic cameras in many wireless sensor network applications (surveillance, industrial monitoring, etc.). In this paper, we propose a multi-mode System-on-Chip (SoC) Field-Programmable Gate Arrays (FPGA) architecture capable to satisfy the high computational demand while providing wireless communication for remote control and monitoring. This architecture produces real-time acoustic images of 240 × 180 resolution scalable to 640 × 480 by exploiting the multithreading capabilities of the hard-core processor. Furthermore, timing cost for different operational modes and for different resolutions are investigated to maintain a real time system under Wireless Sensor Networks constraints.

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CABE: A Cloud-Based Acoustic Beamforming Emulator for FPGA-Based Sound Source Localization

Cited by 2 publications

References 37 publications

XCycles Backprojection Acoustic Super-Resolution

XCycles Backprojection Acoustic Super-Resolution

M3-AC: A Multi-Mode Multithread SoC FPGA Based Acoustic Camera

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