Monthly Archives: March 2023

Allgemein

The emotion cube

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There is a multitude of ways to model emotions, and some of them are collected in the EmotionML vacabularies.
Really popular with engineers and non-psychologists are two approaches:

  • discreet categories like anger, sadness, fear or joy, often associated with an intensity.
  • continuous dimensions like valence/pleasure, arousal or dominance

The emotion cube maps the emotional categories to a three dimensional space:

nkululeko, tutorial

Nkululeko: how to augment the training set

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To do data augmentation with Nkululeko, you can use the augment or the aug_train interface.
The difference is that the former only augments samples, whereas the latter augments the training set of a configuration and then immediately performs the training, including the augmented files.

In the AUGMENT section of your configuration file, you specify the method and name of the output list of file

  • traditional: is the classic augmentation, e.g. by cropping data or adding a bit of noise. We use the audiomentations package for this
  • random-splice: is a special method introduced in this paper that randomly splices and re-connects the audio samples
[AUGMENT]
# select the samples to augment: either train, test, or all
sample_selection = train
# select the method(s)
augment = ['traditional', 'random_splice']
# file name to store the augmented data (can then be added to training)
result = augmented.csv

and then call the interface:

python -m nkululeko.augment --config myconfig.ini

or

python -m nkululeko.aug_train--config myconfig.ini

if you want to run a training in the same run.

Currently, apart from random-splicing, Nkululeko simply uses the audiomentations module, i.e.:

[AUGMENT]
augment = ['traditional']
augmentations = Compose([
AddGaussianNoise(min_amplitude=0.001, max_amplitude=0.05),
Shift(p=0.5),
BandPassFilter(min_center_freq=100.0, max_center_freq=6000),])

These manipulations are applied randomly to your training set.

With respect to the random_splicing method, you can adjust two parameters:

  • p_reverse: probability of some samples to be in reverse order (default: 0.3)
  • top_db: top dB level for silence to be recognized (default: 12)

This configuration, for example, would distort the samples much more than the default:

[AUGMENT]
augment = ['random_splice']
p_reverse = .8
top_db = 6

You should find the augmented files in the storage folder of the result folder of your experiment and could listen to them there.

Once you augmentations have been processed, you can add them to the training in a new experiment:

[DATA]
databases = ['original data', 'augment']
augment = my_augmentations.csv
augment.type = csv
augment.split_strategy = train
course_content

Supervised vs. unsupervised

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Supervised vs. unsupervised

means the distinction whether your training data is annotated (or labeled) with respect to your task. An example: If you want to build a machine learner for human age estimation based on speech, you might give an algorithm a lot of examples of human speech annotated with the age of the person. This would be your training data and the approach would be supervised (by the age annotations). With unsupervised learning, you would give an algorithm simply a lot of human speech data and might ask it to cluster the data, based on differences. And might hope that the resulting clusters coincide with age.

Nkululeko exercise

-> Nkululeko: install the Berlin Emodb

This database contains examples of labels:

  • emotion and gender labels as categorical data, for classification
  • age labels as numerical data, for regression