This is one of a series of posts about how to use nkululeko.
If you're unfamilar with nkululelo, you might want to start here.

This post is about cross database experiments, i.e. training a classifier on one database and test it on another, something that happens quite often with real life situations.

In this post I will only talk about the config file, the python file can be re-used.

I'll walk you through the sections of the config file (all options here):
The first section deals with general setup:

[EXP]
# root is the base directory for the experiment relative to the python call
root = ./experiment_1/
# mainly a name for the top folder to store results (inside root)
name = cross_data

Next, the DATA section might look like this

# declare which databases to use
databases = ['emodb', 'polish']
# specify the location of the data
emodb = <path to the database>
polish = <path to the database>
# we split one database as specified
emodb.split_strategy = specified
# here is the test, the training set is disregarded
emodb.test.tables = ['emotion.categories.test.gold_standard']
# the whole of the polish database is used for train
polish.split_strategy = train
# the target label for the experiment
target = emotion
# we need to unify the labels of both databases
emodb.mapping = {'anger':'angry', 'happiness':'happy', 'sadness':'sad', 'neutral':'neutral'}
polish.mapping = {'anger':'angry', 'joy':'happy', 'sadness':'sad', 'neutral':'neutral'}
# and these are the labels we want to distinguidh
labels = ['angry', 'happy', 'neutral', 'sad']

The features section, better explained in this post

[FEATS]
type = os

The classifiers section, better explained in this post

[MODEL]
type = xgb

Again, you might want to plot the final distribution of categories per train and test set:

[PLOT]
value_counts = True

This is one of a series of posts about how to use nkululeko.

If you're unfamilar with nkululelo, you might want to start here.

This post is about machine classification (as opposed to regression problems) and an introduction how to combine different features sets with different classifiers.

I'll walk you through the sections of the config file (all options here):
The first section deals with general setup:

[EXP]
# root is the base directory for the experiment relative to the python call
root = ./experiment_1/
# mainly a name for the top folder to store results (inside root)
name = exp_A
# needed only for neural net classifiers
#epochs = 100
# needed only for classifiers with random initialization
# runs = 3 

The DATA section deals with the data sets:

[DATA]
# list all the databases  you will be using
databases = ['emodb']

# state the path to the audformat root folder
emodb = /home/felix/data/audb/emodb

# split train and test based on different random speakers
emodb.split_strategy = speaker_split

# state the percentage of test speakers (in this case 4 speakers, as emodb only has 10 speakers)
emodb.testsplit = 40

# the target label that should be classified
target = emotion

# the categories you are interested in
labels = ['anger', 'boredom', 'disgust', 'fear', 'happiness', 'neutral', 'sadness']

The next section deals with the features that should be used by the classifier.

[FEATS]
# the type of features to use
type = ['os']

The choice of currently implemented features is described here:

You can combine feature sets, i.e. they will be concatenated column-wise, e.g. like so:

[FEATS]
# the type of features to use
type = ['os', 'praat']

Next comes the MODEL section which deals with the classifier:

[MODEL]
# the main thing to sepecify is the kind of classifier:
type = xgb

Choices are described here

Models can not be combined, but you can do ensemble learning