def get_rtf_matrix(
psd_speeches,
psd_noises,
diagonal_loading: bool = True,
ref_channel: int = 0,
rtf_iterations: int = 3,
use_torch_solver: bool = True,
diag_eps: float = 1e-7,
eps: float = 1e-8,
):
"""Calculate the RTF matrix with each column the relative transfer function
of the corresponding source.
""" # noqa: H405
assert isinstance(psd_speeches, list) and isinstance(psd_noises, list)
rtf_mat = cat(
[
get_rtf(
psd_speeches[spk],
tik_reg(psd_n, reg=diag_eps, eps=eps)
if diagonal_loading else psd_n,
reference_vector=ref_channel,
iterations=rtf_iterations,
use_torch_solver=use_torch_solver,
) for spk, psd_n in enumerate(psd_noises)
],
dim=-1,
)
# normalize at the reference channel
return rtf_mat / rtf_mat[..., ref_channel, None, :]
def test_cat(dim):
if is_torch_1_9_plus:
wrappers = [ComplexTensor, torch.complex]
modules = [FC, torch]
else:
wrappers = [ComplexTensor]
modules = [FC]
for complex_wrapper, complex_module in zip(wrappers, modules):
mat1 = complex_wrapper(torch.rand(2, 3, 4), torch.rand(2, 3, 4))
mat2 = complex_wrapper(torch.rand(2, 3, 4), torch.rand(2, 3, 4))
ret = cat([mat1, mat2], dim=dim)
ret2 = complex_module.cat([mat1, mat2], dim=dim)
assert complex_module.allclose(ret, ret2)