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A unified music identification system can achieve robust performance across track and version identification tasks with just 10 seconds of audio input.
Models trained on the new DiVers dataset show remarkable resilience to noisy and acoustically diverse inputs, outperforming traditional benchmarks.
OMAR-RQ sets a new open-source benchmark for music audio understanding, outperforming existing self-supervised models across a diverse set of music information retrieval tasks.
