$ lpc -P 36 -W 45 -O 15 HBSe_20161221T010133.wav
Number of classes: 1
class '': 1
HBSe_20161221T010133.wav
lpaOnSignal: P=36 numSamples=18368474 sampleRate=32000 winSize=1440 offset=480 T=38265
data/predictors/_/HBSe_20161221T010133.prd: '': predictor saved
$ vq.learn -P 36 -e 0.0005 data/predictors/_/HBSe_20161221T010133.prd
Plot the general evaluation:
$ cb.plot_evaluation.py data/codebooks/_/eps_0.0005.rpt.csv
M passes DDprm σ inertia
0 2 7 0.329847 1.669760 85563.055636
1 4 19 0.258477 3.662945 67812.819378
2 8 26 0.198647 7.851806 58449.267547
3 16 32 0.157429 11.032102 52835.579481
4 32 16 0.130626 15.396460 49804.951420
5 64 17 0.110648 20.375099 47672.295169
6 128 17 0.096532 24.747489 46300.641095
7 256 16 0.084483 30.756691 45173.663627
8 512 15 0.074696 36.942795 44299.052234
9 1024 14 0.065547 43.420272 43505.964302
10 2048 12 0.056605 51.227322 42754.019571
Cell cardinality and distortions for M=1024:
$ cb.plot_cards_dists.py data/codebooks/_/eps_0.0005_M_1024.cbook.cards_dists.csv
As a scatter plot:
$ cb.plot_cards_dists.py --scatter data/codebooks/_/eps_0.0005_M_1024.cbook.cards_dists.csv
Extract k_1 and k_2 from the training vectors:
$ prd.show -k -r 1-2 data/predictors/_/HBSe_20161221T010133.prd > data/predictors/_/HBSe_20161221T010133.prd.kk.csv
and from the codebooks:
$ for M in 0002 0004 0008 0016 0032 0064 0128 0256 0512 1024; do
cb.show -r 1-2 data/codebooks/_/eps_0.0005_M_$M.cbook > data/codebooks/_/eps_0.0005_M_$M.cbook.kk.csv
done
With the above set of reflection coefficients, let's generate a few "k_1 vs. k_2" scatter plots:
$ for M in 0002 0008 0032 0128 0512; do
cb.plot_reflections.py data/predictors/_/HBSe_20161221T010133.prd.kk.csv data/codebooks/_/eps_0.0005_M_$M.cbook.kk.csv
done
(Note: A maximum of 8000 training vectors, randomly chosen in each case, are plotted.)
M=2:
M=8:
M=32:
M=128:
M=512:
Similar reflection coefficient inspection but now with 3 coefficients:
$ prd.show -k -r 1-3 data/predictors/_/HBSe_20161221T010133.prd > data/predictors/_/HBSe_20161221T010133.prd.kkk.csv
$ cb.show -r 1-3 data/codebooks/_/eps_0.0005_M_1024.cbook > data/codebooks/_/eps_0.0005_M_1024.cbook.kkk.csv
$ cb.plot_reflections.py data/predictors/_/HBSe_20161221T010133.prd.kkk.csv data/codebooks/_/eps_0.0005_M_1024.cbook.kkk.csv
df_training points = 38265
df_training plotted points = 8000
df_codebook points = 1024