2018-09-13
-
some "touches", but I'm now focusing on the C implementation
-
add ToEcoz2, an ad hoc conversion tool export sequences to previous ecoz2 system.
2018-09-07
-
hmm.learn: more micro optimization using while loops
-
hmm.learn: save model every minute (not upon every refinement)
-
some compiler flags for optimization
-
hmm.learn: allocate various matrices only once
-
hmm: check env var RANDOM_SEED to set seed for random values, which facilitates performance evaluation upon changes in code. For example:
RANDOM_SEED=0 ecoz hmm.learn -N 10 -a 0.005 data/sequences/TRAIN/M256/bellow/*
-
hmm.learn: micro optimization for sequence product probability calculation
-
classifySequence: use futures to speed up probability calculation across de hmm models
-
include sequence filename oin report of model rankings
-
ex2: a second, also preliminary exercise with 19 classes from the 10 labelled song files
-
hmm.classify -sr #: only show until corresponding model
-
fix mistake in extracting description column repeating ex2..
-
vp.learn: add -take option (for T max number of symbols)
-
lpc: add -minpc and -take options
-
hmm.classify: show marker for corresponding model when using -sr option
2018-09-06
-
some adjustments while starting exercise with 10 sound files
- hmm.classify: use digit as a marker to show ranking during progress (starting with '0' as correct classification, and using 'x' when ranking is >= 10)
- vq.quantize follows the TRAIN/TEST destination pattern depending on whether the input predictor files have 'TRAIN/' or 'TEST/' in their paths
- new -split option for lpc command, which allows to put the generated predictors into two different training and test subsets.
- sig.xtor: ignore selections with empty description
-
refer to
data/
(instead of../data/
) as base data directory -
update readme and move exercise description to exercises/ex1/
-
refact: move Vq* elements to ecoz.vq package
-
refact: some general renaming (eg., "lpa" for analysis, "lpc" for actual coding)
2018-09-05
-
hmm.classify: -sr option now with argument to indicate number of highest ranked models to show (with probabilities) for each unrecognized sequence
-
first complete HMM exercise with separation of training and test sequences
-
more condensed confusion matrix by using indices to class names
-
hmm.classify: ignore sequences with no hmm model for associated class. This allows to run, for example:
ecoz hmm.classify -hmm data/hmms/N10__M128/*.hmm -seq data/sequences/M128/*/*
when there may not be hmm models for all class names indicated in the given sequences. -
readme: update instructions of basic "closed" test
-
lpc: use simple name in given className path. This facilitates running like this:
ecoz lpc -classes data/signals/*
2018-09-04
-
put predictor files under
predictors/P%d/
-
put quantized sequences under
sequences/M%d/
-
put trained HMM models under
hmms/N%d__M%d/
-
remove trailing insignificant zeroes when saving hmm model. The default BigDecimal.toString method generates representations with many such zeros making the resulting models unnecessarily huge.
-
good preliminary results
-
hmm.classify: show progress with colored dots depending on ranked models
-
adjustments/clarifications related with codebook's
raas
2018-09-03
- implement hmm.learn and hmm.classify. Good initial results
- initial
- implement vq.classify
- capture class name in codebook generation
2018-08-31
- implement vq.quantize
- fix: do gain normalize autocorrelation (not predictor coefficients)
- fix growCodebook; generate report; initial codebooks
- fix: save autocorrelations for predictor file (not the predictor coefficients themselves)
2018-08-30
-
more on initial vq.learn implementation; dealing with empty cells.
ecoz lpc -classes "groan + purr" "descending shriek" "descending moan" "ascending shriek" _ ecoz lpc -classes bark groan grunts gurgle purr trill "purr (D)" "modulated cry" "gurgle?" "gurgle + descending shriek" ecoz vq.learn -p prefijo data/predictors/*/*.prd
2018-08-29
- preparations for vq.learn and vq.quantize
- some more predictor file handling
2018-08-28
- initial commits with preliminaries for selection extraction from wav file, linear prediction, and predictor file handling