From West to East: Who can understand the music of the others better?
Charilaos Papaioannou Emmanouil Benetos Alexandros Potamianos
Recent developments in MIR have led to several benchmark deep learning models
whose embeddings can be used for a variety of downstream tasks. At the same
time, the vast majority of these models have been trained on Western pop/rock
music and related styles. This leads to research questions on whether these
models can be used to learn representations for different music cultures and
styles, or whether we can build similar music audio embedding models trained on
data from different cultures or styles. To that end, we leverage transfer
learning methods to derive insights about the similarities between the
different music cultures to which the data belongs to. We use two Western music
datasets, two traditional/folk datasets coming from eastern Mediterranean
cultures, and two datasets belonging to Indian art music. Three deep audio
embedding models are trained and transferred across domains, including two
CNN-based and a Transformer-based architecture, to perform auto-tagging for
each target domain dataset. Experimental results show that competitive
performance is achieved in all domains via transfer learning, while the best
source dataset varies for each music culture. The implementation and the
trained models are both provided in a public repository.