CultureMERT: Continual Pre-Training for Cross-Cultural Music
  Representation Learning
Authors: name: Angelos-Nikolaos Kanatas, name: Charilaos Papaioannou, name: Alexandros Potamianos

Recent advances in music foundation models have improved audio representation
learning, yet their effectiveness across diverse musical traditions remains
limited. We introduce CultureMERT-95M, a multi-culturally adapted foundation
model developed to enhance cross-cultural music representation learning and
understanding. To achieve this, we propose a two-stage continual pre-training
strategy that integrates learning rate re-warming and re-decaying, enabling
stable adaptation even with limited computational resources. Training on a
650-hour multi-cultural data mix, comprising Greek, Turkish, and Indian music
traditions, results in an average improvement of 4.9% in ROC-AUC and AP across
diverse non-Western music auto-tagging tasks, surpassing prior
state-of-the-art, with minimal forgetting on Western-centric benchmarks. We
further investigate task arithmetic, an alternative approach to multi-cultural
adaptation that merges single-culture adapted models in the weight space. Task
arithmetic performs on par with our multi-culturally trained model on
non-Western auto-tagging tasks and shows no regression on Western datasets.
Cross-cultural evaluation reveals that single-culture models transfer with
varying effectiveness across musical traditions, whereas the multi-culturally
adapted model achieves the best overall performance. To support research on
world music representation learning, we publicly release CultureMERT-95M and
CultureMERT-TA-95M, fostering the development of more culturally aware music
foundation models.