Skip to content

Latest commit

 

History

History
135 lines (88 loc) · 5.16 KB

README.md

File metadata and controls

135 lines (88 loc) · 5.16 KB
Raw

Note. In this case, the source sound files:

$ ls -l ~/Desktop/MBARI/Soundscape/HumpbackSong_6_good_songs/
... 35M Feb 14  2018 HBSe_20161221T010133.wav
... 30M Feb 14  2018 HBSe_20161221T014717.wav
... 34M Feb 14  2018 HBSe_20161221T020427.wav
... 43M Feb 14  2018 HBSe_20170109T204800.wav
... 33M Feb 14  2018 HBSe_20170116T055614.wav
... 71M Feb 14  2018 HBSe_20170128T231621.wav

are located outside of this repository.

LPC analysis

$ lpc -P 36 -W 45 -O 15 ~/Desktop/MBARI/Soundscape/HumpbackSong_6_good_songs/*.wav
Number of classes: 1
class 'HumpbackSong_6_good_songs': 6
  /Users/carueda/Desktop/MBARI/Soundscape/HumpbackSong_6_good_songs/HBSe_20161221T014717.wav
lpaOnSignal: P=36 numSamples=15674431 sampleRate=32000 winSize=1440 offset=480 T=32653
data/predictors/HumpbackSong_6_good_songs/HBSe_20161221T014717.prd: 'HumpbackSong_6_good_songs': predictor saved

  /Users/carueda/Desktop/MBARI/Soundscape/HumpbackSong_6_good_songs/HBSe_20170128T231621.wav
lpaOnSignal: P=36 numSamples=36977463 sampleRate=32000 winSize=1440 offset=480 T=77034
data/predictors/HumpbackSong_6_good_songs/HBSe_20170128T231621.prd: 'HumpbackSong_6_good_songs': predictor saved

  /Users/carueda/Desktop/MBARI/Soundscape/HumpbackSong_6_good_songs/HBSe_20170109T204800.wav
lpaOnSignal: P=36 numSamples=22612312 sampleRate=32000 winSize=1440 offset=480 T=47106
data/predictors/HumpbackSong_6_good_songs/HBSe_20170109T204800.prd: 'HumpbackSong_6_good_songs': predictor saved

  /Users/carueda/Desktop/MBARI/Soundscape/HumpbackSong_6_good_songs/HBSe_20161221T010133.wav
lpaOnSignal: P=36 numSamples=18368474 sampleRate=32000 winSize=1440 offset=480 T=38265
data/predictors/HumpbackSong_6_good_songs/HBSe_20161221T010133.prd: 'HumpbackSong_6_good_songs': predictor saved

  /Users/carueda/Desktop/MBARI/Soundscape/HumpbackSong_6_good_songs/HBSe_20170116T055614.wav
lpaOnSignal: P=36 numSamples=17342609 sampleRate=32000 winSize=1440 offset=480 T=36128
data/predictors/HumpbackSong_6_good_songs/HBSe_20170116T055614.prd: 'HumpbackSong_6_good_songs': predictor saved

  /Users/carueda/Desktop/MBARI/Soundscape/HumpbackSong_6_good_songs/HBSe_20161221T020427.wav
lpaOnSignal: P=36 numSamples=18001105 sampleRate=32000 winSize=1440 offset=480 T=37500

Codebook generation

With the 6 files, this amounts to 268,686 training vectors.

$ vq.learn -P 36 -e 0.0005 data/predictors/HumpbackSong_6_good_songs/*.prd

Plot the general evaluation:

$ cb.plot_evaluation.py data/codebooks/_/eps_0.0005.rpt.csv
       M  passes     DDprm          σ        inertia
0      2       9  0.285415   1.453891  522828.495346
1      4       9  0.236424   2.079603  457206.493195
2      8      15  0.196693   3.558782  415913.646977
3     16      14  0.167026   5.153438  384482.300009
4     32      20  0.141351   7.154483  359349.556103
5     64      19  0.121253   9.554444  342508.921965
6    128      16  0.107120  12.136631  332027.907051
7    256      17  0.096203  14.315932  324612.268032
8    512      15  0.087421  16.705644  318889.487475
9   1024      15  0.079596  19.233455  313963.162855
10  2048      14  0.072313  22.115393  309482.011895

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:

$ for prd in data/predictors/HumpbackSong_6_good_songs/*.prd; do
  prd.show -k -r 1-2 $prd >> k1k2.csv
done

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 k1k2.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.