FPGA-Based Architectures for Acoustic Beamforming with Microphone Arrays: Trends, Challenges and Research Opportunities

Silva, Bruno da; Braeken, An; Touhafi, Abdellah

doi:10.3390/computers7030041

Cited by 12 publications

(10 citation statements)

References 77 publications

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“…Field programmable gate arrays (FPGAs) and digital signal processors (DSP) have been widely demonstrated in the literature for acoustic source localization [32]. A detailed review on the use of FPGA platforms for acoustic beamforming using microphone arrays has been presented in [33]. The paper highlighted current trends, difficulties and future opportunities in the given area.…”

Section: Existing Methodsmentioning

confidence: 99%

Microphone Array and Raspberry Pi Interfacing for Real-time DOA Estimation and Tracking of Audio Sources

Patwari¹

2020

IJETER

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Microphone arrays are commonly used for Direction of Arrival (DOA) estimation of audio sources. This paper presents the design and implementation of a real-time embedded system (RTES) for audio source localization. A commercially available microphone array assembly (consisting of four microphones) has been interfaced to Raspberry Pi 3B for estimating the arrival angles of audio/sound/acoustic sources. The acoustic signals received a teach of the microphones are used for DOA estimation using the Generalized Cross Correlation Phase Transform (GCC-PHAT) algorithm. The estimated DOA is then displayed on the serial monitor. Servo motors are interfaced to the Raspberry Pi and are programmed to point towards the estimated direction of the audio source (on the basis of the estimated DOA value). The estimated DOA value is also updated to the cloud-based server Firebase using the inbuilt Wi-Fi on Raspberry Pi, and can be remotely accessed through a smartphone running on android platform. The proposed system has been designed and tested successfully and it is observed that it provides accurate DOA estimation and audio source tracking in real-time. This has various uses in applications like voice assistant robots, machine audition, hearing aids, source remixing, augmented reality headsets, security cameras, sound tracking (sonar sensor) devices, bird identification, mammal localization and so on.

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Section: Existing Methodsmentioning

confidence: 99%

Microphone Array and Raspberry Pi Interfacing for Real-time DOA Estimation and Tracking of Audio Sources

Patwari¹

2020

IJETER

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“…The majority of the publications related to MEMS microphones or microphone arrays mainly discuss microphone technologies, with around 4% of the overall number of publications describing FPGA-based systems using microphone arrays. We believe that FPGAs are currently underexploited in this area [13].…”

Section: Related Workmentioning

confidence: 99%

“…Microphone array: digital MEMS microphones, especially PDM MEMS ones, have become popular when building microphone arrays [13]. Besides the multiple advantages of MEMS microphones, some of their features, such as low-power modes, make them interesting candidates for reconfigurable acoustic beamforming architectures.…”

Section: Stages Of Reconfigurable Architectures For Time-domain Acousmentioning

confidence: 99%

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

et al. 2019

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Many applications rely on MEMS microphone arrays for locating sound sources prior to their execution. Those applications not only are executed under real-time constraints but also are often embedded on low-power devices. These environments become challenging when increasing the number of microphones or requiring dynamic responses. Field-Programmable Gate Arrays (FPGAs) are usually chosen due to their flexibility and computational power. This work intends to guide the design of reconfigurable acoustic beamforming architectures, which are not only able to accurately determine the sound Direction-Of-Arrival (DoA) but also capable to satisfy the most demanding applications in terms of power efficiency. Design considerations of the required operations performing the sound location are discussed and analysed in order to facilitate the elaboration of reconfigurable acoustic beamforming architectures. Performance strategies are proposed and evaluated based on the characteristics of the presented architecture. This power-efficient architecture is compared to a different architecture prioritizing performance in order to reveal the unavoidable design trade-offs.

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“…Analog microphones are sampled with Analog to Digital Converters (ADCs) and streamed in PCM format in the D&S algorithms [47]. One strategy consists in filtering the samples with a low-pass Finite Impulse Response (FIR) filter before displaying the result [48].…”

Section: Cabe: Cloud-based Acoustic Beamforming Emulatormentioning

confidence: 99%

“…Acoustic decimation and filtering must be applied to retrieve the acoustic information. Several strategies have been carried out [47]. A common strategy consists in decimating the PDM signal by means of a CIC filter.…”

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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FPGA-Based Architectures for Acoustic Beamforming with Microphone Arrays: Trends, Challenges and Research Opportunities

Cited by 12 publications

References 77 publications

Microphone Array and Raspberry Pi Interfacing for Real-time DOA Estimation and Tracking of Audio Sources

Microphone Array and Raspberry Pi Interfacing for Real-time DOA Estimation and Tracking of Audio Sources

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

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

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