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Recent developments in MIR have led to several benchmark deep learning modelswhose embeddings can be used for a variety of downstream tasks. At the sametime, the vast majority of these models have been trained on Western pop/rockmusic and related styles. This leads to research questions on whether thesemodels can be used to learn representations for different music cultures andstyles, or whether we can build similar music audio embedding models trained ondata from different cultures or styles. To that end, we leverage transferlearning methods to derive insights about the similarities between thedifferent music cultures to which the data belongs to. We use two Western musicdatasets, two traditional/folk datasets coming from eastern Mediterraneancultures, and two datasets belonging to Indian art music. Three deep audioembedding models are trained and transferred across domains, including twoCNN-based and a Transformer-based architecture, to perform auto-tagging foreach target domain dataset. Experimental results show that competitiveperformance is achieved in all domains via transfer learning, while the bestsource dataset varies for each music culture. The implementation and thetrained models are both provided in a public repository.

From West to East: Who can understand the music of the others better?