10 citations · 18 across the 6 of their papers we have counts for
6 papers
Monaural speech enhancement on drone via Adapter based transfer learning
Xingyu Chen, Hanwen Bi, Wei-Ting Lai +1
Monaural Speech enhancement on drones is challenging because the ego-noise from the rotating motors and propellers leads to extremely low signal-to-noise ratios at onboard micropho…
Circumvent spherical Bessel function nulls for open sphere microphone arrays with physics informed neural network
Fei Ma, Thushara D. Abhayapala, Prasanga N. Samarasinghe
Open sphere microphone arrays (OSMAs) are simple to design and do not introduce scattering fields, and thus can be advantageous than other arrays for implementing spatial acoustic…
Sound Field Estimation around a Rigid Sphere with Physics-informed Neural Network
Xingyu Chen, Fei Ma, Amy Bastine +2
Accurate estimation of the sound field around a rigid sphere necessitates adequate sampling on the sphere, which may not always be possible. To overcome this challenge, this paper…
Spatial Upsampling of Head-Related Transfer Functions Using a Physics-Informed Neural Network
Fei Ma, Thushara D. Abhayapala, Prasanga N. Samarasinghe +1
Head-related transfer function (HRTF) capture the information that a person uses to localize sound sources in space, and thus is crucial for creating personalized virtual acoustic…
A time-domain nearfield frequency-invariant beamforming method
Fei Ma, Thushara D. Abhayapala, Prasanga N. Samarasinghe
Most existing beamforming methods are frequency-domain methods, and are designed for enhancing a farfield target source over a narrow frequency band. They have found diverse applic…
Real-time separation of non-stationary sound fields on spheres
Fei Ma, Wen Zhang, Thushara D. Abhayapala
The sound field separation methods can separate the target field from the interfering noises, facilitating the study of the acoustic characteristics of the target source, which is…