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LPC analysis

$ 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

Codebook generation

$ 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