Researches

Acoustic Sensing

Meta-Speaker: Acoustic Source Projection by Exploiting Air Nonlinearity


This paper proposes Meta-Speaker, an innovative speaker capable of projecting audible sources into the air with a high level of manipulability. Unlike traditional speakers that emit sound waves in all directions, Meta-Speaker can manipulate the granularity of the audible region, down to a single point, and can manipulate the location of the source. Additionally, the source projected by Meta-Speaker is a physical presence in space, allowing both humans and machines to perceive it with spatial awareness. Meta-Speaker achieves this by leveraging the fact that air is a nonlinear medium, which enables the reproduction of audible sources from ultrasounds. Meta-Speaker comprises two distributed ultrasonic arrays, each transmitting a narrow ultrasonic beam. The audible source can be reproduced at the intersection of the beams. We present a comprehensive profiling of Meta-Speaker to validate the high manipulability it offers. We prototype Meta-Speaker and demonstrate its potential through three applications: anchor-free localization with a median error of 0.13 m, location-aware communication with a throughput of 1.28 Kbps, and acoustic augmented reality where users can perceive source direction with a mean error of 9.8 degrees. This work is accepted by ACM MobiCom 2023.

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MicNest: Long-Range Instant Acoustic Localization of Drones in Precise Landing


We present MicNest: an acoustic localization system enabling precise landing of aerial drones. Drone landing is a crucial step in a drone’s operation. In MicNest, multiple microphones are deployed on a landing platform in carefully devised configurations. The drone carries a speaker transmitting purposefully designed acoustic pulses. The drone may be localized as long as the pulses are correctly detected. Doing so is challenging: i) because of limited transmission power, propagation attenuation, background noise, and propeller interference, the Signal-to-Noise Ratio (SNR) of received pulses is intrinsically low; ii) the pulses experience non-linear Doppler distortion due to the physical drone dynamics while airborne; iii) as location information is to be used during landing, the processing latency must be reduced to effectively feed the flight control loop. To tackle these issues, we design a novel pulse detector, Matched Filter Tree (MFT), whose idea is to convert pulse detection to a tree search problem. We further present three practical methods to accelerate tree search jointly. Our real-world experiments show that MicNest is able to localize a drone 120 m away with 0.53% relative localization error at 20 Hz location update frequency. This work is accepted by ACM SenSys 2022. For more details, please check out our website: http://micnest.github.io.

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AIM: Acoustic Inertial Measurement for Indoor Drone Localization and Tracking


Location information is crucial for drone operation, regardless of the application and target deployment environment. In outdoor settings, GPS is arguably mainstream. However, indoor drone localization and tracking are arguably a crucial, yet unsolved challenge: in GPS-denied environments, existing approaches enjoy limited applicability, especially in Non-Line of Sight (NLoS), require extensive environment instrumentation, or demand considerable hardware/software changes on drones. In this work, we present Acoustic Internal Measurement (AIM), a technique to localize and track drones using the acoustic signals naturally produced by the drone propellers. AIM is entirely passive: it requires no additional hardware and no software changes on the drones. Due to the features of acoustic signals, AIM also works in NLoS scenarios with much better performance than the few existing systems that would be inapplicable in these settings. Further, AIM works with a single microphone array but may be extended with ease to support spaces with arbitrary ranges and layouts by deploying distributed arrays. This work appears in Sensys 2022.

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MotorBeat: Acoustic Communication for Home Appliances via Variable Pulse Width Modulation


More and more home appliances are now connected to the Internet, thus enabling various smart home applications. However, a critical problem that may impede the further development of smart home is overlooked: Small appliances account for the majority of home appliances, but they receive little attention and most of them are cut off from the Internet. To fill this gap, we propose MotorBeat, an acoustic communication approach that connects small appliances to a smart speaker. Our key idea is to exploit direct current (DC) motors, which are common components of small appliances, to transmit acoustic messages. We design a novel scheme named Variable Pulse Width Modulation (V-PWM) to drive DC motors. MotorBeat achieves the following 3C goals: (1) Comfortable to hear, (2) Compatible with multiple motor modes, and (3) Concurrent transmission. We implement MotorBeat with commercial devices and evaluate its performance on three small appliances and ten DC motors. The results show that the communication range can be up to 10 m. This work is accepted by ACM Ubicomp 2022.

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Symphony: Localizing Multiple Acoustic Sources with a Single Microphone Array


In this work, we study the problem of concurrently localizing multiple acoustic sources with a smart device, e.g. a smart speaker like Amazon Alexa or Alibaba Tmall Genie. The existing approaches either can only localize a single acoustic source, or require deploying a distributed network of microphone arrays to function.

Our proposal called Symphony is the first approach to tackle the above problem with a single microphone array. The insight behind Symphony is that the geometric layout of microphones on the array determines the unique relationship among signals from the same source along the same arriving path, while the source’s location determines the DoAs (direction-of-arrival) of signals along different arriving paths. Symphony therefore includes a geometry-based filtering module to distinguish signals from different acoustic sources, and a coherence-based module to identify signals from the same source.

This work appears in the proceedings of ACM SenSys 2020.

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ChordMics: Acoustic Signal Purification with Distributed Microphones


Existing beamforming systems are able to extract the signal transmitted from certain directions. However, since microphones are centrally deployed, these systems have limited coverage and low spatial resolution. We overcome the above limitations and present ChordMics, a distributed beamforming system. By leveraging the spatial diversity of the distributed microphones, ChordMics is able to extract the acoustic signal from arbitrary points. The evaluation demonstrate that ChordMics can deliver higher SINR than the centralized microphone array. The average performance gain is up to 15dB. This work appears in ICCCN 2020.

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