This repository holds the sample-train-packs which are used to create AFEC's default category and classification models.

Please note that only the extracted audio-features for the machine learning models and folder structure is included here. The actual audio-files of the classification packs partly got lost and also can't be uploaded because of unclear copyright issues.

But you can use the extracted features (afec-ll.csv) to test various classification models, and can use the given folder structure for your own classification experiments.

The ./Scripts folder contains various python scripts to experiment with Keras / Tensorflow classification models.
See individual train_XXX.py files for details and pip dependencies. Please note: AFEC does not use those Keras models - they are only used for the sake of experimentation.

Please Note: The datasets are stored via git lfs, so first make sure they are checked out via git lfs pull.

• OneShot-vs-Loops: Class in AFEC: splits sounds into either Loop or OneShot sounds.

• OneShot-Categories: Categories in AFEC: detects categories for all sounds which got classified as OneShots.

The AFEC crawler executable uses two bundled shark models to evaluate the classifiers. The shark models get built from the sample-packs in this repository.

To update the existing or built new models, you can use the Create%SAMPLE_PACK_NAME%Model.[bat|sh] scripts in the main AFEC main repository at Scripts/ModelCreator.

They do the following for each sample pack:

# Create or update low level descriptors for the sample pack - when necessary.
$ Crawler -l low $PATH_TO_SAMPLE_PACK -o $PATH_TO_SAMPLE_PACK/features.db

# Load descriptors from $PATH_TO_SAMPLE_PACK and train the networks. Write 
# resulting binary shark model to the crawler's Resource/Models path, using
# the sample pack folder base-name as model filename.
$ ModelCreator $PATH_TO_SAMPLE_PACK/features.db 

To test modified sample packs or when adding completely new categories, the ModelTester executable can be used to evaluate and debug the performance of the newly created model and classes. To do so, you can use the Test%SAMPLE_PACK_NAME%Model.bat scripts in the main AFEC main repository at Scripts/ModelCreator.

Similar to the ModelCreator, they do the following:

# Create or update low level descriptors for the sample pack, when necessary.
$ Crawler -l low $PATH_TO_SAMPLE_PACK -o $PATH_TO_SAMPLE_PACK/features.db

# Load descriptors from PATH_TO_SAMPLE_PACK and train a simplified version of the model
# .
# When done, a summary for each class's prediction accuracy will be printed to the console. 
# Additionally it copies two files to the $PATH_TO_SAMPLE_PACK root folder:
# - Confusion.csv: the classification confusion matrix (see https://en.wikipedia.org/wiki/Confusion_matrix)
# - PredictionErrors.csv: a list of all audio files which have been mispredicted
$ ModelTester $PATH_TO_SAMPLE_PACK/features.db 

The prediction accuracy tells you how good each class/category performs in overall in the tests. Ideally all classes should have an accuracy of > 80% to be somewhat usable later on. Confusion.csv gives you more details about that, by also listing as which other categories the mispredicted samples got detected instead. PredictionErrors.csv shows more details about each single sound that got mispredicted, including all calculated class weights for all categories for this specific sample.

Confusion.csv usually is a first good overview of how the model performs. PredictionErrors.csv then is useful to double-check a categories' (possibly modified) content in detail. If a sample often gets confused with some other category, this usually is a good hint that either the category can't be detected properly (ir isn't well defined) or that a specific sample simply is not a good example for the category: Ideally each sample pack's category folder should include a variety of prototype sounds that fit into this category, but all those prototypes should also not smear too much into other categories prototypes.

Note that when adding a completely new category, it's often necessary to double-check all old class prototypes again, to ensure that the old category prototypes are not represented by the new category as well. Confusion.csv again should give you a good hint here and PredictionErrors.csv what exactly seems to be the problem.