fast_bss_eval

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.

seamlessly 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

Author

Robin Scheibler

Quick Start

Install

# from pypi
pip install fast-bss-eval

# or from source
git clone https://github.com/fakufaku/fast_bss_eval
cd fast_bss_eval
pip install -e .

Use

Assuming you have multichannel signals for the estmated and reference sources
stored in wav format files names my_estimate_file.wav and
my_reference_file.wav, respectively, you can quickly evaluate the bss_eval
metrics as follows.

from scipy.io import wavfile
import fast_bss_eval

# open the files, we assume the sampling rate is known
# to be the same
fs, ref = wavfile.read("my_reference_file.wav")
_, est = wavfile.read("my_estimate_file.wav")

# compute the metrics
sdr, sir, sar, perm = fast_bss_eval.bss_eval_sources(ref.T, est.T)

Benchmark

This package is significantly faster than other packages that also allow
to compute bss_eval metrics such as 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).

Citation

If you use this package in your own research, please cite our paper describing it.

@misc{scheibler_sdr_2021,
  title={SDR --- Medium Rare with Fast Computations},
  author={Robin Scheibler},
  year={2021},
  eprint={2110.06440},
  archivePrefix={arXiv},
  primaryClass={eess.AS}
}

License

2021 (c) Robin Scheibler, LINE Corporation

All of this code is released under MIT License with the exception of fast_bss_eval/torch/hungarian.py which is under 3-clause BSD License.

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