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A fast implementation of bss_eval metrics for blind source separation



black
tests

Do you have a zillion BSS audio files to process and it is taking days ?
Is your simulation never ending ?

Fear no more! fast_bss_eval is here to help you!

fast_bss_eval is a fast implementation of the bss_eval metrics for the
evaluation of blind source separation. Our implementation of the bss_eval
metrics has the following advantages compared to other existing ones.

  • seemlessly works with both numpy arrays and pytorch tensors
  • very fast
  • can be even faster by using an iterative solver (add use_cg_iter=10 option to the function call)
  • differentiable via pytorch
  • can run on GPU via pytorch

mir_eval or sigsep/bsseval.
We did a benchmark using numpy/torch, single/double precision floating point
arithmetic (fp32/fp64), and using either Gaussian elimination or a conjugate
gradient descent

(solve/CGD10).

MIT License.

GitHub

View Github


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