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arxiv: 2008.06048 · v2 · pith:CHGQUUBE · submitted 2020-08-13 · cs.SD · cs.LG· cs.MM

MMM : Exploring Conditional Multi-Track Music Generation with the Transformer

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classification cs.SD cs.LGcs.MM
keywords multi-trackmusicmusicalsequencetransformercontroleventsgeneration
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We propose the Multi-Track Music Machine (MMM), a generative system based on the Transformer architecture that is capable of generating multi-track music. In contrast to previous work, which represents musical material as a single time-ordered sequence, where the musical events corresponding to different tracks are interleaved, we create a time-ordered sequence of musical events for each track and concatenate several tracks into a single sequence. This takes advantage of the Transformer's attention-mechanism, which can adeptly handle long-term dependencies. We explore how various representations can offer the user a high degree of control at generation time, providing an interactive demo that accommodates track-level and bar-level inpainting, and offers control over track instrumentation and note density.

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  1. Towards Real-Time Human-AI Musical Co-Performance: Accompaniment Generation with Latent Diffusion Models and MAX/MSP

    cs.SD 2026-04 unverdicted novelty 6.0

    A latent diffusion model with consistency distillation generates real-time instrumental accompaniment from live context audio, integrated with MAX/MSP for feasible human-AI co-performance.