Fei Ma @ ANU || Scholar || Github || Linkedin ||
Table of Contents
Spatial Acoustics
Active noise control over space
AR/VR acoustics
PhD, Electrical Engineering, Australian National Univeristy, 2015 – 2020
Thesis topic: Active noise control over space
Supervisor : Thushara Abhayapala, Professor at Australian National Univeristy, Australia
Supervisor : Wen Zhang, Profossor at Northwestern Polytechnical University, China
Master & Bachelor, Electrical Engineering, Harbin Institute of Technology, 2009 – 2015
Research fellow @ ANU, Aug. 2020 – now
Research intern @ Facebook Inc., FRL Jul. 2019 – Dec. 2019
Toturing the Digital Signal Processing Course @ ANU, Mar. 2019 – Jun. 2019
2020_J2 “Multiple circular arrays of vector sensors for 3D sound field analysis,” IEEE/ACM Trans. on Audio, Speech, and Lang. Proces., vol. 28, pp. 529–539, 2020.
2020_J1 “Active control of outgoing broadband noise fields in rooms,” IEEE/ACM Trans. on Audio, Speech, and Lang. Proces., vol. 28, pp. 529–539, 2020.
2019_J1 “Real-time separation of non-stationary sound fields on spheres,” J. Acoust. Soc. Am., vol. 146, no. 1, p. 11, Jul. 2019.
The drone noise and localization dateset @ Speech modeling for facilitating communication, Franch
Clean and noisy in-flight audio recordings continuously annotated with the 3D position of the target sound source using an accurate motion capture system;
signals such as the rotational speed of individual rotors and inertial measurements.
Audio-visual sensing from a quadcopter @ Queen Mary University of London, UK
The dataset was collected using a small circular array with 8 microphones and a camera mounted on the quadcopter.
The camera view was used to facilitate the annotation of the sound-source positions and can also be used for multi-modal sensing tasks.
Acoustic Interactions for Robot Audition - Unmanned Aerial Systems @IIMAS, Mexico
Recordings of the DJI Matrice 100 flying drone in an outdoor environment.
Comments:
These data are all measured using microphone arrays on the drones.
UIUC UIUC Applied Aerodynamics Group
Lin Wang Queen Mary University of London, UK
Audio-visual sensing from a quadcopter: dataset and baselines for source localization and sound enhancement Li-2018
Microphone-array ego-noise reduction algorithms for auditory micro aerial vehicles Li-2017
Kazuhiro Nakadai Tokyo Institute of Technology, Japan
Noise correlation matrix estimation for improving sound source localization by multirotor UAV NK-2013
Design of UAV-Embedded Microphone Array System for Sound Source Localization in Outdoor Environments NK-2017
Caleb Rascon Mexico
An Evaluation Corpus for Audio Processing in Unmanned Aerial System AIRA-UAS
Detection of nearby UAVs using a multi-microphone array on board a UAV
Antoine Deleforge Franch
Dataset and methods for uav-embedded sound source localization Dregon
Oliver Jokisch Germany
Seongkyu Lee Department of Mechanical and Aerospace Engineering@ University of California Davis
aeroacoustics including drone rotor noise
Analytic formulation and numerical implementation of an acoustic pressure gradient prediction AF08
Acoustic Scattering in the Time Domain Using an Equivalent Source Method AS12
Andrew Ning FLOW Lab at Brigham Young University
aerodynamics and optimization with applications in aircraft design and wind energy systems
Rotor-on-Rotor Aeroacoustic Interactions of Multirotor in Hover ROR-2020
the Vortex Particle Method for drone noise simulation VPM
Michael Joseph Kigan, Yusuke Hioka, Acoustics Research Centre at Univeristy of Auckland
Investigating the aeroacoustic noise produced by an aerofoil
Ana Vieira Delft University of Technology, Netherland
Experimental characterization of noise radiation from a ducted propeller of an unmanned aerial vehicle Duct
Review on Ducted Fans for Compound Rotorcraft roto
W. Nathan Alexander Aeroacoustics at Virginia Tech
Experimental study of quadcopter acoustics and performance at static thrust conditions EC-16
Propeller Noise in Confined Anechoic and Open Environments PN-20
Broadband rotor noise predictions using a time domain approach BR-16
Flyover Noise of Multi-Rotor sUAS FN-19
Sheryl Grace Aeroacoustics at Boston University
Prediction of small quadrotor blade induced noise PSQ-19
Xin ZHANG Hong Kong University of Science and Technology
Multi-rotor noise scattering by a drone fuselage MR2019
Tonal noise of single and multiple propellers Tonal-2017
Acoustic characteristics of a quad-copter under realistic flight conditions ACO-19
Measurement of Noise from a Moving Drone Using a Phased Array Microphone System MO-17
Tonal Noise Characteristics of Two Small-Scale Propellers TNC-17
Michael Barad NASA, Acoustic Branch
Predicting Quadcopter Drone Noise Using the Lattice Boltzmann Method SC-19
Acoustic Characterization and Prediction of Representative, Small-Scale Rotary-Wing Unmanned Aircraft System Components AC-16
Tonal Noise Prediction of a Distributed Propulsion Unmanned Aerial Vehicle TN-18
A Summary of NASA Research Exploring the Acoustics of Small Unmanned Aerial Systems NA18
A Summary of the NASA Design Environment for Novel Vertical Lift Vehicles Project NADL
Auralization of air vehicle noise for community noise assessment NAAA
Comment:
The Ffowcs Williams-Hawkings Equation
Multiple-thread Audio Processing Framework @ C++
Spherical Array Processing Library : matlab source code
Some useful software and their source code
ARM+DSP
C++, Julia, real-time operating system
ARM+FPGA
powerful
fixed-point!
parallel DSP
analogy, I2S, TDM, PDM
ARM
cheap
powerful
easy to use
TDM, i2s, PDM, PMW, analogy
DSP+ARM
powerful and professional
Acoustic vector sensors @ Microflown
Velocity sensor
ICS-52000 @ TDK
Digital MEMS microphone
TDM output, 16 makes an array
High SNR, sensitivity tolerance +/- 1 dB, no need for calibration !
Professional microphone pair with caliberation
CHiME speech separation and recoginition challenge
Detection and classification of acoustic scence and events
Communication science lab @ NTT
Building a new technical infrastructure that can connect humans to information
FLOW Lab @ Brigham Young University
Aerodynamics and optimization with applications in aircraft design and wind energy systems.
UIUV Applied Aerodynamics Group
understand about the fundamentals of fluid flow and applying that to make a better aerodynamic system
Parsimony and New Algorithms for Audio & Signal Modeling
Develop mathematically founded and algorithmically efficient techniques to model, acquire and process high-dimensional signals, with a strong emphasis on acoustic data.
Auditory Signal Processing and Hearing Devices @ Univerisity of Oldenburg
Algorithms that put the cocktail party effect into hearing aids
Institute of Communication Systems @ RWTH
spatial audio, active noise cancellation
Augmented Instruments Lab @ Queen Mary University
Salford Innovation Research Centre (SIRC)
Audio research group @ Tampere Univeristy
Spatial audio, source separation and enhancement
Acoustic Lab @ Aalto Univeristy
audio processing and spatial sound technologies
Computing and Audio Research Laboratory @ Sydney Univeristy
Spatial Audio
Acoustics Research Centre @ Univeristy Auckland
theoretical, computational and experimental research on acoustics
Dotterel develops noise reduction and audio recording technology for UAVs.
X-craft a New Zealand company that designs, develops and tests drones.
Microflown develops acoustic vector sensors.
dBV represents the level compared to 1 Volt RMS. 0dBV = 1V. There is no reference to impedance.
dBu represents the level compared to 0.775 Volts RMS with an unloaded, open circuit, source (u = unloaded).
dBm represents the power level compared to 1 mWatt. This is a level compared to 0.775 Volts RMS across a 600 Ohm load impedance. Note that this is a measurement of power, not a measurement of voltage.
dbFS - relative to digital full-scale.
dB SPL - A measure of sound pressure level.