Search Results for author:
Found 4 papers, 1 papers with code
Sound field reconstruction using neural processes with dynamic kernels
Accurately representing the sound field with the high spatial resolution is critical for immersive and interactive sound field reproduction technology.
Circumvent spherical Bessel function nulls for open sphere microphone arrays with physics informed neural network
A PINN models the measurement of an OSMA and predicts the sound field on another sphere whose radius is different from that of the OSMA.
Spatial Upsampling of Head-Related Transfer Functions Using a Physics-Informed Neural Network
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 experiences.
A time-domain nearfield frequency-invariant beamforming method
The time-domain implementation makes the beamformer output suitable for further use by real-time applications, the nearfield focusing enables the beamforming method to suppress an interference even if it is in the same direction as the target source, and the frequency-invariant beampattern makes the beamforming method suitable for enhancing the target source over a broad frequency band.
