I have been searching for the way we can subtract signal better since I could see the acoustic coupling signal remains in the target signal even though there are no coherence between them.
I changed the training time which is used to decide wiener filter.
I have total 10 minutes data, and the wiener filter was decided using the whole data before.
(Right: the performance with the data when the triangular sound was created. Left: the performance with the data when the gaussian sound was created.)
I found that the acoustic signal can be fully subtracted above 40 Hz when the training time is short. This means the transfer functions between the acoustic signals and MCF signal change.
However, if the wiener filter is decided with short-time training, the performances at lower frequencies get worse. This is because wiener filter do not have enough low-frequency information.
So, I would like to find the way to combine the short-time training merit and long-time training merit. It should be useful to subtract the broad-band coupling noise.