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Music performance synthesis aims to synthesize a musical score into a naturalperformance. In this paper, we borrow recent advances in text-to-speechsynthesis and present the Deep Performer -- a novel system for score-to-audiomusic performance synthesis. Unlike speech, music often contains polyphony andlong notes. Hence, we propose two new techniques for handling polyphonic inputsand providing a fine-grained conditioning in a transformer encoder-decodermodel. To train our proposed system, we present a new violin dataset consistingof paired recordings and scores along with estimated alignments between them.We show that our proposed model can synthesize music with clear polyphony andharmonic structures. In a listening test, we achieve competitive qualityagainst the baseline model, a conditional generative audio model, in terms ofpitch accuracy, timbre and noise level. Moreover, our proposed modelsignificantly outperforms the baseline on an existing piano dataset in overallquality.

Deep Performer: Score-to-Audio Music Performance Synthesis