SoundCompass: a distributed MEMS microphone array-based sensor for sound source localization

Sound source localization is a well-researched subject with applications ranging from localizing sniper fire in urban battlefields to cataloging wildlife in rural areas. One critical application is the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human health. Current noise mapping techniques often fail to accurately identify noise pollution sources, because they rely on the interpolation of a limited number of scattered sound sensors. Aiming to produce accurate noise pollution maps, we developed the SoundCompass, a low-cost sound sensor capable of measuring local noise levels and sound field directionality. Our first prototype is composed of a sensor array of 52 Microelectromechanical systems (MEMS) microphones, an inertial measuring unit and a low-power field-programmable gate array (FPGA). This article presents the SoundCompass's hardware and firmware design together with a data fusion technique that exploits the sensing capabilities of the SoundCompass in a wireless sensor network to localize noise pollution sources. Live tests produced a sound source localization accuracy of a few centimeters in a 25-m2 anechoic chamber, while simulation results accurately located up to five broadband sound sources in a 10,000-m2 open field

A miniature, lowpower , intelligent sensor node for persistent acoustic surveillance

ABSTRACT The desire for persistent, long term surveillance and covertness places severe constraints on the power consumption of a sensor node. To achieve the desired endurance while minimizing the size of the node, it is imperative to use application-specific integrated circuits (ASICs) that deliver the required performance with maximal power efficiency while minimizing the amount of communication bandwidth needed. This paper reviews our ongoing effort to integrate several micropower devices for low-power wake-up detection, blind source separation and localization and pattern classification, and demonstrate the utility of the system in relevant surveillance applications. The capabilities of each module are presented in detail along with performance statistics measured during recent experiments

Thin On-Sensor Nanophotonic Array Cameras

Today's commodity camera systems rely on compound optics to map light originating from the scene to positions on the sensor where it gets recorded as an image. To record images without optical aberrations, i.e., deviations from Gauss' linear model of optics, typical lens systems introduce increasingly complex stacks of optical elements which are responsible for the height of existing commodity cameras. In this work, we investigate flat nanophotonic computational cameras as an alternative that employs an array of skewed lenslets and a learned reconstruction approach. The optical array is embedded on a metasurface that, at 700~nm height, is flat and sits on the sensor cover glass at 2.5~mm focal distance from the sensor. To tackle the highly chromatic response of a metasurface and design the array over the entire sensor, we propose a differentiable optimization method that continuously samples over the visible spectrum and factorizes the optical modulation for different incident fields into individual lenses. We reconstruct a megapixel image from our flat imager with a learned probabilistic reconstruction method that employs a generative diffusion model to sample an implicit prior. To tackle scene-dependent aberrations in broadband, we propose a method for acquiring paired captured training data in varying illumination conditions. We assess the proposed flat camera design in simulation and with an experimental prototype, validating that the method is capable of recovering images from diverse scenes in broadband with a single nanophotonic layer.Comment: 18 pages, 12 figures, to be published in ACM Transactions on Graphic

Distributed Microphone Array System for Two-way Audio Communication

Tässä työssä esitellään hajautettu mikrofoniryhmäjärjestelmä kahdensuuntaisessa äänikommunikaatiossa. Järjestelmän tavoitteena on paikallistaa hallitseva puhuja ja tallentaa puhesignaali mahdollisimman korkealaatuisesti. Työssä esiteltävässä järjestelmässä jokainen mikrofoniryhmä toimii polynomirakenteella parametrisoituna keilanmuodostajana (PBF), joka mahdollistaa jatkuvan keilanohjauksen. Hallitsevan puhelähteen suunta päätellään PBF:n jokaisen keilan ulostulotehoista. Lopuksi yhdistämällä jokaisen PBF:n kaikkien keilojen ulostulotehot muodostetaan avaruudellinen todennäköisyysfunktio (SLF), jonka suurin arvo määrää puhujan paikan. Puhesignaali tallennetaan ohjaamalla puhujaa lähinnä olevan PBF:n keila puhujan suuntaan. Tässä työssä esiteltävän järjestelmän toiminta arvioitiin simuloidulla ja mitatulla datalla. Arvionti näyttää, että toteutettu järjestelmä pystyy paikallistamaan puhujan noin 40 cm paikannustarkkuudella ja järjestelmä vaimentaa muista suunnista tulevia häiriölähteitä noin 15 dB. Lopuksi järjestelmä toteutettiin reaaliakaisena systeeminä Pure Data signaalinkäsittelyympäristössä.In this work a distributed microphone array system for two-way audio communication is presented. The goal of the system is to locate the dominant speaker and capture the speech signal with highest possible quality. In the presented system each microphone array works as a Polynomial Beamformer (PBF) thus enabling continuous beam steering. The output power of each PBF beam is used to determine the direction of the dominant speech source. Finally, a Spatial Likelihood Function (SLF) is formed by combining the output beam powers of each microphone array and the speaker is determined to be in the point that has highest value of SLF. The audio signal capture is done by steering the closest microphone array to the direction of the speaker. The presented audio capture front-end was evaluated with simulated and measured data. The evaluation shows that the implemented system gives approximately 40 cm localization accuracy and 15 dB attenuation of interference sources. Finally the system was implemented to run in real-time in the Pure Data signal processing environment