Sevag H

Yes, it is being used (the default of 200).

Yes, `umxhq(device="cpu",niter=0)` works well. The total memory usage is at 29GB, while with niter=1, it grows to >64 and gets killed. I guess this is a duplicate of https://github.com/sigsep/open-unmix-pytorch/issues/7 which...

If you would like, I can take a look with memory_profiler and see if I can create any savings to contribute to this project? Song looks like it's 7:10: ```...

I just tried disabling grad on my audio tensor, didn't save much. Some heavy lines from my profiling: ``` 278 21639.691 MiB 1933.609 MiB 30 v = torch.mean(torch.abs(y[..., 0, :])...

I thought I could be smart and only apply Wiener on `max_bin = bandwidth_to_bin(16000)`. It saves ~5-10 GB of memory but loses a bit of SDR.

OK, it seems to be working. Here's a piece of code, hacked together from https://github.com/sigsep/sigsep-mus-oracle/blob/master/IRM.py and unmix: ```python def atan2(y, x): r"""Element-wise arctangent function of y/x. copied from umx, replace...

It's pretty underwhelming. Here is an evaluation of 4 tracks from the MUSDB18-HQ test set, with IRM1, IRM2, IBM1, IBM2, and the new one, "MPI" (Mixed Phase Inversion), with the...

Open-Unmix is not the first time I've seen the source estimate magnitude + mix phase inversion. It's also used in the CDAE source separation algorithm (https://arxiv.org/abs/1703.08019) but I'm still curious...

Here: https://github.com/sevagh/mss-oracle-experiments#oracle-performance-of-mpi-mix-phase-inversion Apologies if there is a lot of irrelevant code (related to the NSGT), but I hope the specific part of the new "Mixed Phase Inversion" oracle makes sense...

Also, I suppose SDR is not necessarily the king of metrics - we can see dramatically better ISR on the mix-phase (but that could be a consequence of its reduced...