Researchers have developed a new system that converts expressive drum grids, a detailed MIDI format, into realistic drum audio. This method utilizes a Transformer model to predict discrete codes from a neural audio codec, which are then decoded into sound. Experiments with codecs like EnCodec, DAC, and X-Codec show that the choice of audio representation significantly impacts the quality of the synthesized drums. The system was trained and evaluated on the E-GMD dataset, demonstrating codec-token prediction as a viable approach for percussive synthesis. AI
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IMPACT Introduces a new method for generating realistic percussive audio from symbolic music representations, potentially impacting music production tools.
RANK_REASON Academic paper detailing a novel approach to audio synthesis using machine learning. [lever_c_demoted from research: ic=1 ai=1.0]