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LPC analysis

$ lpc -P 36 -W 45 -O 15 HBSe_20170128T231621.wav
Number of classes: 1
class '': 1
  HBSe_20170128T231621.wav
lpaOnSignal: P=36 numSamples=36977463 sampleRate=32000 winSize=1440 offset=480 T=77034
data/predictors/_/HBSe_20170128T231621.prd: '': predictor saved

Codebook generation

$ vq.learn -P 36 -e 0.0005 data/predictors/_/HBSe_20170128T231621.prd

Plot the general evaluation:

$ cb.plot_evaluation.py data/codebooks/_/eps_0.0005.rpt.csv

       M  passes     DDprm          σ        inertia
0      2       7  0.255785   1.587956  143107.263731
1      4       9  0.211432   2.192094  125029.021765
2      8      27  0.164426   5.526398  109486.976613
3     16      16  0.140328   7.775847  103058.072623
4     32      13  0.116596  11.300068   97311.543605
5     64      15  0.101224  14.506913   94083.550532
6    128      16  0.090683  17.409458   92055.505677
7    256      15  0.082132  20.192721   90490.788718
8    512      16  0.074236  23.523729   89079.286266
9   1024      16  0.066392  27.985164   87714.217618
10  2048      13  0.059158  32.833571   86486.074742

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_20170128T231621.prd > data/predictors/_/HBSe_20170128T231621.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_20170128T231621.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_20170128T231621.prd > data/predictors/_/HBSe_20170128T231621.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_20170128T231621.prd.kkk.csv data/codebooks/_/eps_0.0005_M_1024.cbook.kkk.csv
df_training points = 77034
df_training plotted points = 8000
df_codebook points = 1024

Cell shapes

See P2/README.md.