Acoustic DoA Estimation by One Unsophisticated Sensor

Badawy, Dalia El; Vetterli, Martin

doi:10.1007/978-3-319-53547-0_9

Cited by 9 publications

(14 citation statements)

References 11 publications

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“…In [64], a small vertical wall of varying shape is placed next to a microphone which changes the frequency response for different directions of sound. A few recent works [17,18] place small structures like legos and cubes around a microphone to produce scattering. These works on monaural localization either keep a dictionary of possible source models or predict the source model before estimating the DoA.…”

Section: Related Workmentioning

confidence: 99%

“…Past work has explored localization by fingerprinting multipath environments and analyzing nearby reflections [64]. Probably closest to our work is [18] that places objects in a 60 × 60 cm space with a microphone at the center. This work shows that the sound scattered by the nearby objects holds directional cues and can be processed to find its direction of arrival.…”

Section: Introductionmentioning

confidence: 99%

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Owlet

Garg

Bai

Roy

2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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This paper presents a low-power and miniaturized design for acoustic direction-of-arrival (DoA) estimation and source localization, called Owlet. The required aperture, power consumption, and hardware complexity of the traditional array-based spatial sensing techniques make them unsuitable for small and power-constrained IoT devices. Aiming to overcome these fundamental limitations, Owlet explores acoustic microstructures for extracting spatial information. It uses a carefully designed 3D-printed metamaterial structure that covers the microphone. The structure embeds a direction-specific signature in the recorded sounds. Owlet system learns the directional signatures through a one-time in-lab calibration. The system uses an additional microphone as a reference channel and develops techniques that eliminate environmental variation, making the design robust to noises and multipaths in arbitrary locations of operations. Owlet prototype shows 3.6 • median error in DoA estimation and 10 median error in source localization while using a 1.5 × 1.3 acoustic structure for sensing. The prototype consumes less than 100 ℎ of the energy required by a traditional microphone array to achieve similar DoA estimation accuracy. Owlet opens up possibilities of low-power sensing through 3D-printed passive structures. CCS CONCEPTS• Computer systems organization → Embedded and cyberphysical systems; Sensors and actuators.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Owlet

Garg

Bai

Roy

2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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“…In another study [7], a metamaterial-coated device with a diameter of 40 cm and a dictionary of noise prototypes were used to localize known noise sources. In our previous work [9], we used an omnidirectional sensor surrounded by cubes of different sizes and a dictionary of spectral prototypes to localize speech sources.…”

Section: A Related Workmentioning

confidence: 99%

“…Combining HRTF-like directional selectivity with source models has already been explored in the literature [6], [7], [8], [9]. For example, in one study [8], a small microphone enclosure was used to localize one source with the help of a Hidden Markov Model (HMM) trained on a variety of sounds including speech.…”

Section: A Related Workmentioning

confidence: 99%

Direction of Arrival With One Microphone, a Few LEGOs, and Non-Negative Matrix Factorization

Badawy

2018

IEEE/ACM Trans. Audio Speech Lang. Process.

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Conventional approaches to sound source localization require at least two microphones. It is known, however, that people with unilateral hearing loss can also localize sounds. Monaural localization is possible thanks to the scattering by the head, though it hinges on learning the spectra of the various sources. We take inspiration from this human ability to propose algorithms for accurate sound source localization using a single microphone embedded in an arbitrary scattering structure. The structure modifies the frequency response of the microphone in a direction-dependent way giving each direction a signature. While knowing those signatures is sufficient to localize sources of white noise, localizing speech is much more challenging: it is an ill-posed inverse problem which we regularize by prior knowledge in the form of learned non-negative dictionaries. We demonstrate a monaural speech localization algorithm based on non-negative matrix factorization that does not depend on sophisticated, designed scatterers. In fact, we show experimental results with ad hoc scatterers made of LEGO bricks. Even with these rudimentary structures we can accurately localize arbitrary speakers; that is, we do not need to learn the dictionary for the particular speaker to be localized. Finally, we discuss multisource localization and the related limitations of our approach.

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“…Based on specific technology, it is possible to categorize methodologies for smartphone-based indoor pedestrian self-positioning systems into two distinct groups: (1) infrastructure-based systems that use auxiliary equipment or a cooperation between nodes to realize target tracking [1,2,3,4,5,6,7,8,9], and (2) the infrastructure-free systems that realize pedestrian self-positioning using only the information provided by the smartphone carried on one’s person [9,10,11,12,13,14,15,16,17,18,19,20]. However, when using the former, the pedestrian is likely to experience difficulties in position acquisition when the cooperative information is unavailable.…”

Section: Introductionmentioning

confidence: 99%

Indoor Pedestrian Self-Positioning Based on Image Acoustic Source Impulse Using a Sensor-Rich Smartphone

Song

Wang

Qiu

et al. 2018

Sensors

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The ubiquity of sensor-rich smartphones provides opportunities for a low-cost method to track indoor pedestrians. In this situation, pedestrian dead reckoning (PDR) is a widely used technology; however, its cumulative error seriously affects its accuracy. This paper presents a method of combining infrastructure-free indoor acoustic self-positioning with PDR self-positioning, which verifies the rationality of PDR results through the acoustic constraint between a sound source and its image sources. We further determine the first-order echo delay measurements, thus obtaining the mobile user position. We verify that the proposed method can achieve a continuous self-positioning median error of 0.19 m, and the error probability below 0.12 m is 54.46%, which indicates its ability to eliminate PDR error, as well as its adaptability to environmental disturbances.

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Acoustic DoA Estimation by One Unsophisticated Sensor

Cited by 9 publications

References 11 publications

Owlet

Owlet

Direction of Arrival With One Microphone, a Few LEGOs, and Non-Negative Matrix Factorization

Indoor Pedestrian Self-Positioning Based on Image Acoustic Source Impulse Using a Sensor-Rich Smartphone

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