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View Code? Open in Web Editor NEWA Python toolkit for sound source separation.
Home Page: https://sound-source-separation-python.readthedocs.io/en/v0.1.7/
License: Apache License 2.0
A Python toolkit for sound source separation.
Home Page: https://sound-source-separation-python.readthedocs.io/en/v0.1.7/
License: Apache License 2.0
The settings of code-cov are in default.
Some of the settings may be changed or customized.
AuxIn IVA-ISS, demixing filters are not computed, so _reset
will result in failure.
Lines 150 to 151 in 8da9325
The links to each notebook will be helpful.
Write links in README.md like
[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tky823/ssspy/blob/main/notebooks/bss/GaussILRMA-IP1.ipynb)
The arguments should_<do>_*
are too long.
How about use_*
instead?
Now, ssspy
selects updating pair using itertools.combinations(range(self.n_sources), 2)
.
This behavior is different from papers.
isort
reorders the python modules, which improves the intelligibility of this library.
Create ssspy.linalg.eigh
by using NumPy.
I have to add a test for 4 sources separation.
ssspy.bss._update_spatial_model:update_by_ip2
returns different result from old one.
The test is important to keep the quality of code high.
However, it takes a lot of time to test codes.
Practical solutions
np.linalg.slogdet(X)
is more robust than np.log(np.linalg.det(X))
A sample dataset is necessary to verify separation methods correctly work.
This function is also required to tests/
.
ssspy.transform.whiten
only supports complex inputs.
Now, code coverages are updated multiple times. This is time-consuming.
The separation quality of GaussILRMA-IP2 is limited.
I have to investigate the cause.
Now, we define callback functions and classes in each test script.
In FastICA, FastIVA, and FasterIVA, the input should be whitened before the fixed-point-iteration-based algorithm is applied.
However, the timing of whitening is different among these methods.
Auxiliary-function-based demixing filter update is common in FDICA, IVA, and ILRMA.
We can create submodule or function to handle this.
Now, the permutation solver is based on the correlation between frequencies.
Degree-of-arrival based method may be useful.
Jupyter notebooks may help users understand how to utilize this library.
Notebooks
Documentation is important to users.
Now, I haven't installed document tests, but I want to do it.
This issue includes measure of document coverage.
Auxiliary-function-based FDICA should be tested.
MNMF and its derivations
np.random.rand
is legacy style.
Use random number generator like:
rng = np.random.default_rng()
rng.random(size=(5, 2))
It is useful to add extras_require option to setup.cfg
for dev and docs.
List up implemented methods.
Pull request template helps us open PR in a standardized format.
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