A fast implementation of bss_eval metrics for blind source separation


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


MIT License.


View Github

Leave a Reply

Your email address will not be published. Required fields are marked *

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

GIPHY App Key not set. Please check settings

Build an Embeddable Widget using Preact and the Shadow DOM

Improving Angular tests by enabling Angular testing module teardown