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fftpack → fft #381

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fftpack → fft #381

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a658f4e
scipy.fft, separation acceleration. fixes #373
bmcfee Apr 9, 2024
909cd35
fixing lengths in irfft calls
bmcfee Apr 9, 2024
8f554ab
bumped smallest scipy for proper fast fft length computation
bmcfee Apr 9, 2024
5bf8333
tracking down accuracy regression in separation tests
bmcfee Apr 9, 2024
9d8b899
reverting keyword nextfastlen patch to positional arg, support scipy …
bmcfee Apr 9, 2024
df25077
fixing a transpose error
bmcfee Apr 9, 2024
253c237
fixed error reporting in separation test failures
bmcfee Apr 9, 2024
881ff7d
blacked the package
bmcfee Apr 9, 2024
e8c837c
better handling of linalg warnings in separation, switch to scipy.linalg
bmcfee Apr 10, 2024
5281268
blacked tests
bmcfee Apr 10, 2024
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71 changes: 43 additions & 28 deletions mir_eval/separation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -46,7 +46,7 @@
"""

import numpy as np
import scipy.fftpack
import scipy.fft
from scipy.linalg import toeplitz
from scipy.signal import fftconvolve
import collections
Expand Down Expand Up @@ -692,17 +692,17 @@ def _project(reference_sources, estimated_source, flen):

# computing coefficients of least squares problem via FFT ##
# zero padding and FFT of input data
reference_sources = np.hstack((reference_sources, np.zeros((nsrc, flen - 1))))
estimated_source = np.hstack((estimated_source, np.zeros(flen - 1)))
n_fft = int(2 ** np.ceil(np.log2(nsampl + flen - 1.0)))
sf = scipy.fftpack.fft(reference_sources, n=n_fft, axis=1)
sef = scipy.fftpack.fft(estimated_source, n=n_fft)
# reference_sources = np.hstack((reference_sources, np.zeros((nsrc, flen - 1))))
# estimated_source = np.hstack((estimated_source, np.zeros(flen - 1)))
n_fft = scipy.fft.next_fast_len(nsampl + flen - 1, True)
sf = scipy.fft.rfft(reference_sources, n=n_fft, axis=1)
sef = scipy.fft.rfft(estimated_source, n=n_fft)
# inner products between delayed versions of reference_sources
G = np.zeros((nsrc * flen, nsrc * flen))
for i in range(nsrc):
for j in range(nsrc):
ssf = sf[i] * np.conj(sf[j])
ssf = np.real(scipy.fftpack.ifft(ssf))
ssf = scipy.fft.irfft(ssf, n=n_fft)
ss = toeplitz(np.hstack((ssf[0], ssf[-1:-flen:-1])), r=ssf[:flen])
G[i * flen : (i + 1) * flen, j * flen : (j + 1) * flen] = ss
G[j * flen : (j + 1) * flen, i * flen : (i + 1) * flen] = ss.T
Expand All @@ -711,15 +711,21 @@ def _project(reference_sources, estimated_source, flen):
D = np.zeros(nsrc * flen)
for i in range(nsrc):
ssef = sf[i] * np.conj(sef)
ssef = np.real(scipy.fftpack.ifft(ssef))
ssef = scipy.fft.irfft(ssef, n=n_fft)
D[i * flen : (i + 1) * flen] = np.hstack((ssef[0], ssef[-1:-flen:-1]))

# Computing projection
# Distortion filters
try:
C = np.linalg.solve(G, D).reshape(flen, nsrc, order="F")
except np.linalg.linalg.LinAlgError:
C = np.linalg.lstsq(G, D)[0].reshape(flen, nsrc, order="F")
with warnings.catch_warnings():
warnings.filterwarnings("error", category=scipy.linalg.LinAlgWarning)
try:
C = scipy.linalg.solve(G, D).reshape(flen, nsrc, order="F")
except (
np.linalg.linalg.LinAlgError,
scipy.linalg.LinAlgError,
scipy.linalg.LinAlgWarning,
):
C = scipy.linalg.lstsq(G, D, cond=None)[0].reshape(flen, nsrc, order="F")
# Filtering
sproj = np.zeros(nsampl + flen - 1)
for i in range(nsrc):
Expand All @@ -743,15 +749,15 @@ def _project_images(reference_sources, estimated_source, flen, G=None):

# computing coefficients of least squares problem via FFT ##
# zero padding and FFT of input data
reference_sources = np.hstack(
(reference_sources, np.zeros((nchan * nsrc, flen - 1)))
)
estimated_source = np.hstack(
(estimated_source.transpose(), np.zeros((nchan, flen - 1)))
)
n_fft = int(2 ** np.ceil(np.log2(nsampl + flen - 1.0)))
sf = scipy.fftpack.fft(reference_sources, n=n_fft, axis=1)
sef = scipy.fftpack.fft(estimated_source, n=n_fft)
# reference_sources = np.hstack(
# (reference_sources, np.zeros((nchan * nsrc, flen - 1)))
# )
# estimated_source = np.hstack(
# (estimated_source.transpose(), np.zeros((nchan, flen - 1)))
# )
n_fft = scipy.fft.next_fast_len(nsampl + flen - 1, True)
sf = scipy.fft.rfft(reference_sources, n=n_fft, axis=1)
sef = scipy.fft.rfft(estimated_source.T, n=n_fft)

# inner products between delayed versions of reference_sources
if G is None:
Expand All @@ -760,7 +766,7 @@ def _project_images(reference_sources, estimated_source, flen, G=None):
for i in range(nchan * nsrc):
for j in range(i + 1):
ssf = sf[i] * np.conj(sf[j])
ssf = np.real(scipy.fftpack.ifft(ssf))
ssf = scipy.fft.irfft(ssf, n=n_fft)
ss = toeplitz(np.hstack((ssf[0], ssf[-1:-flen:-1])), r=ssf[:flen])
G[i * flen : (i + 1) * flen, j * flen : (j + 1) * flen] = ss
G[j * flen : (j + 1) * flen, i * flen : (i + 1) * flen] = ss.T
Expand All @@ -771,7 +777,7 @@ def _project_images(reference_sources, estimated_source, flen, G=None):
for i in range(nchan * nsrc):
for j in range(i + 1):
ssf = sf[i] * np.conj(sf[j])
ssf = np.real(scipy.fftpack.ifft(ssf))
ssf = scipy.fft.irfft(ssf, n=n_fft)
ss = toeplitz(np.hstack((ssf[0], ssf[-1:-flen:-1])), r=ssf[:flen])
G[i * flen : (i + 1) * flen, j * flen : (j + 1) * flen] = ss
G[j * flen : (j + 1) * flen, i * flen : (i + 1) * flen] = ss.T
Expand All @@ -782,17 +788,26 @@ def _project_images(reference_sources, estimated_source, flen, G=None):
for k in range(nchan * nsrc):
for i in range(nchan):
ssef = sf[k] * np.conj(sef[i])
ssef = np.real(scipy.fftpack.ifft(ssef))
ssef = scipy.fft.irfft(ssef, n=n_fft)
D[k * flen : (k + 1) * flen, i] = np.hstack(
(ssef[0], ssef[-1:-flen:-1])
).transpose()

# Computing projection
# Distortion filters
try:
C = np.linalg.solve(G, D).reshape(flen, nchan * nsrc, nchan, order="F")
except np.linalg.linalg.LinAlgError:
C = np.linalg.lstsq(G, D)[0].reshape(flen, nchan * nsrc, nchan, order="F")
with warnings.catch_warnings():
warnings.filterwarnings("error", category=scipy.linalg.LinAlgWarning)

try:
C = scipy.linalg.solve(G, D).reshape(flen, nchan * nsrc, nchan, order="F")
except (
np.linalg.linalg.LinAlgError,
scipy.linalg.LinAlgError,
scipy.linalg.LinAlgWarning,
):
C = scipy.linalg.lstsq(G, D, cond=None)[0].reshape(
flen, nchan * nsrc, nchan, order="F"
)
# Filtering
sproj = np.zeros((nchan, nsampl + flen - 1))
for k in range(nchan * nsrc):
Expand Down
5 changes: 5 additions & 0 deletions tests/test_separation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -434,6 +434,11 @@ def test_separation_images_framewise(separation_data):
imageframe_scores[key],
expected_image_frames[test_data_name],
atol=A_TOL,
), np.max(
np.abs(
np.array(imageframe_scores[key])
- np.array(expected_image_frames[test_data_name])
)
)

# Catch a few exceptions in the evaluate function
Expand Down
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