Owlet: Enabling Spatial Information in Ubiquitous Acoutsic Devices


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-specic 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 10cm median error in source localization while using a 1.52cm x 1.32cm acoustic structure for sensing. The prototype consumes less than 100th 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.
    Owlet vision
    (1) The vision and technical overview of Owlet, a low-power and miniaturized system for extracting spatial information from sound. Owlet uses acoustic microstructures to embed direction specific signatures on the recorded sound and develops a learningbased approach for signature recovery and mapping in real-time.

    Directional filtering
    (3) The concept of passive directional filtering using a stencil of acoustic microstructure. The stencil embeds a directional signature to the recorded sound unique to its direction of arrival (DoA). The spectrum of complex gains represents the signature for further computation.

    (5) Angular diversity of the microphone with and without the microstructure stencil.

    (7) The two-microphone model for eliminating source and environmental dependency.

    Sound approximation
    (2) The concept of using a stencil with direction-speci!c hole patterns and microstructures for passive !ltering of the incoming sound. The stencil embeds a directional response to the recordedsignals.

    stencil design
    (4) Different types of metamaterial stencils used in our experiments.

    frequency response
    (6) Comparison of the diversity in frequency responses (amplitude and phase) of the three types of metamaterial stencils.

    neural network
    (8) The architecture of the proposed CNN model.


  • Owlet: Enabling Spatial Information in Ubiquitous Acoustic Devices, MobiSys'21 [paper] [talk]
  • Demo: Microstructure-guided Spatial Sensing for Low-power IoT, MobiSys'21 [paper]
         (Best demo award, MobiSys 2021)
  • Talk



    Nirupam Roy
    Assistant Professor
    Dept. of CS


    Nakul Garg
    PhD Student
    Dept. of CS


    Yang Bai
    PhD Student
    Dept. of CS