Sometimes you might want to combine databases that are similar, or alike, but don't handle exactly the same phenomena.

Take for example stress and emotion, you don't have enough data that labels stress, but many emotion databases that label anger and happiness. You might try the approach to use angry samples as stressed and happy or neutral as non-stressed.

Taking the usual emodb as example, and famous Susas as a database sampling stressed voices, you can do this like this:

[DATA]
databases = ['emodb', 'susas']

emodb = ./data/emodb/emodb
# indicate where the target values are
emodb.target_tables = ["emotion"]
# rename emotion to stress
emodb.colnames = {"emotion": "stress"}
# only use angry, neutral and happy samples
emodb.filter = [["stress", ["anger", "neutral", "happiness"]]]
# map them to stress
emodb.mapping = {"anger": "stress",  "neutral": "no stress", "happiness": "no stress"}
# and put everything to the training
emodb.split_strategy = train

susas = data/susas/
# map ternary stress labes to binary
susas.mapping = {'0,1':'no stress', '2':'stress'}
susas.split_strategy = speaker_split

target = stress
labels = ["stress", "no stress"]

So Susas will be split into train and test, but the training will be strenghend by the whole of emodb. This usually makes actually more sense if a third database is available for evaluation, because in-domain machine learning in most of the cases always works better than adding out-of-domain data (like we do here with emodb).