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Contextual embeddings can revolutionize how we evaluate expressive MIDI performances, aligning closely with human perception while overcoming traditional metric limitations.
Flow matching models can now generate expressive piano performances that capture nuanced timing, breaking free from the constraints of fixed-length audio samples.
