Design Exploration and Performance Strategies towards Power-Efficient FPGA-Based Architectures for Sound Source Localization

Silva, Bruno da; Segers, Laurent; Braeken, An; Steenhaut, Kris; Touhafi, Abdellah

doi:10.1155/2019/5761235

Cited by 10 publications

(9 citation statements)

References 20 publications

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“…The type and configuration of the filters are selected based on several design considerations related to the parameters such as the sampling frequency(F s ), the maximum supported frequency (F max ) and the decimation factor (D F ) summarized in Table 3. Further considerations about the design of the filter chain are largely discussed in [27], where a complete design-space exploration of possible PDM demodulators is presented.…”

Section: Filter Stagementioning

confidence: 99%

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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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Section: Filter Stagementioning

confidence: 99%

“…The use of digital MEMS PDM microphones provides us an additional flexibility to explore alternative beamforming architectures. For instance, the architecture discussed in [27] provides a significantly better frequency response and a lower power consumption at the cost of a performance reduction.…”

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confidence: 99%

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

et al. 2021

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“…This is especially the case for PDM microphones where the decimation and filtering can be computed prior to the D&S as well as afterwards. Moreover, a hybrid solution has been proposed by da Silva et al [10,55]. Following beamforming sequences are currently supported by the emulator (Figure 5):Delay and Sum + Filtering for analog based microphones,Delay and Sum + CIC + Filtering for PDM based microphones,CIC + Filtering + Delay and Sum for PDM based microphones,CIC + Filtering + Delay and Decimation and Sum.…”

Section: Cabe: Cloud-based Acoustic Beamforming Emulatormentioning

confidence: 99%

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

Segers

Vandendriessche

Vandervelden

et al. 2019

Sensors

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Microphone arrays are gaining in popularity thanks to the availability of low-cost microphones. Applications including sonar, binaural hearing aid devices, acoustic indoor localization techniques and speech recognition are proposed by several research groups and companies. In most of the available implementations, the microphones utilized are assumed to offer an ideal response in a given frequency domain. Several toolboxes and software can be used to obtain a theoretical response of a microphone array with a given beamforming algorithm. However, a tool facilitating the design of a microphone array taking into account the non-ideal characteristics could not be found. Moreover, generating packages facilitating the implementation on Field Programmable Gate Arrays has, to our knowledge, not been carried out yet. Visualizing the responses in 2D and 3D also poses an engineering challenge. To alleviate these shortcomings, a scalable Cloud-based Acoustic Beamforming Emulator (CABE) is proposed. The non-ideal characteristics of microphones are considered during the computations and results are validated with acoustic data captured from microphones. It is also possible to generate hardware description language packages containing delay tables facilitating the implementation of Delay-and-Sum beamformers in embedded hardware. Truncation error analysis can also be carried out for fixed-point signal processing. The effects of disabling a given group of microphones within the microphone array can also be calculated. Results and packages can be visualized with a dedicated client application. Users can create and configure several parameters of an emulation, including sound source placement, the shape of the microphone array and the required signal processing flow. Depending on the user configuration, 2D and 3D graphs showing the beamforming results, waterfall diagrams and performance metrics can be generated by the client application. The emulations are also validated with captured data from existing microphone arrays.

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“…MEMS technology applied to acoustic sensors has developed high-quality microphones with high SNR (signal-to-noise ratio), high sensitivity, and low power consumption [ 42 ]. In sound source localization systems, MEMS microphones are often combined with FPGA-based architectures [ 43 ].…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Using a MEMS Microphone Array for Pedestrian Detection in an Autonomous Emergency Braking System

Izquierdo

Val

Villacorta

2021

Sensors

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Pedestrian detection by a car is typically performed using camera, LIDAR, or RADAR-based systems. The first two systems, based on the propagation of light, do not work in foggy or poor visibility environments, and the latter are expensive and the probability associated with their ability to detect people is low. It is necessary to develop systems that are not based on light propagation, with reduced cost and with a high detection probability for pedestrians. This work presents a new sensor that satisfies these three requirements. An active sound system, with a sensor based on a 2D array of MEMS microphones, working in the 14 kHz to 21 kHz band, has been developed. The architecture of the system is based on an FPGA and a multicore processor that allow the system to operate in real time. The algorithms developed are based on a beamformer, range and lane filters, and a CFAR (Constant False Alarm Rate) detector. In this work, tests have been carried out with different people and in different ranges, calculating, in each case and globally, the Detection Probability and the False Alarm Probability of the system. The results obtained verify that the developed system allows the detection and estimation of the position of pedestrians, ensuring that a vehicle travelling at up to 50 km/h can stop and avoid a collision.

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Design Exploration and Performance Strategies towards Power-Efficient FPGA-Based Architectures for Sound Source Localization

Cited by 10 publications

References 20 publications

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

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

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

Feasibility of Using a MEMS Microphone Array for Pedestrian Detection in an Autonomous Emergency Braking System

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