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Found 3 papers, 1 papers with code
TRUNet: Transformer-Recurrent-U Network for Multi-channel Reverberant Sound Source Separation
We evaluate the network on a realistic and challenging reverberant dataset, generated from measured room impulse responses of an actual microphone array.
DBNET: DOA-driven beamforming network for end-to-end farfield sound source separation
Many deep learning techniques are available to perform source separation and reduce background noise.
Improving auditory attention decoding performance of linear and non-linear methods using state-space model
In this paper, we investigate a state-space model using correlation coefficients obtained with a small correlation window to improve the decoding performance of the linear and the non-linear AAD methods.
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