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ARXIV:2605.10281 · AUDIO SYNTHESIS · SUBMITTED 12 MAY · 20:15 UTC · FRESHNESS FRESH
ARXIV:2605.10281AUDIO SYNTHESISSUBMITTED 12 MAY · 20:15 UTCFRESHNESS FRESHKonstantinos Soiledis · Maximos Kaliakatsos-Papakostas · Dimos Makris · Konstantinos Tsamis · arXiv
A system synthesizes realistic drum audio from expressive drum grids using neural audio codecs and Transformer models.
Opportunity summary
Pain A system synthesizes realistic drum audio from expressive drum grids using neural audio codecs and Transformer models.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
A system synthesizes realistic drum audio from expressive drum grids using neural audio codecs and Transformer models. We propose a system that transforms an expressive drum grid, a time-aligned MIDI representation with microtiming and…
Generating realistic drum audio directly from symbolic representations is a challenging task at the intersection of music perception and machine learning. We propose a system that transforms an expressive drum grid, a time-aligned MIDI…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Overall, our results establish codec-token prediction as an effective route for drum grid-to-audio generation and provide practical insights into selecting audio tokenizers for percussive…
Audio Synthesis moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A system synthesizes realistic drum audio from expressive drum grids using neural audio codecs and Transformer models.
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10.48550/arXiv.2605.10281A system synthesizes realistic drum audio from expressive drum grids using neural audio codecs and Transformer models.
Abstract
Generating realistic drum audio directly from symbolic representations is a challenging task at the intersection of music perception and machine learning. We propose a system that transforms an expressive drum grid, a time-aligned MIDI representation with microtiming and velocity information, into drum audio by predicting discrete codes of a neural audio codec. Our approach uses a Transformer-based model to map the drum grid input to a sequence of codec tokens, which are then converted to waveform audio via a pre-trained codec decoder. We experiment with multiple state-of-the-art neural codecs, namely EnCodec, DAC, and X-Codec, to assess how the choice of audio representation impacts the quality of the generated drums. The system is trained and evaluated on the Expanded Groove MIDI Dataset, E-GMD, a large collection of human drum performances with paired MIDI and audio. We evaluate the fidelity and musical alignment of the generated audio using objective metrics. Overall, our results establish codec-token prediction as an effective route for drum grid-to-audio generation and provide practical insights into selecting audio tokenizers for percussive synthesis.
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Dimensions overall score 7.0
PROBLEM
A system synthesizes realistic drum audio from expressive drum grids using neural audio codecs and Transformer models. We propose a system that transforms an expressive drum grid, a time-aligned MIDI representation with microtiming and velocity information, into drum audio by pr...
METHOD
Generating realistic drum audio directly from symbolic representations is a challenging task at the intersection of music perception and machine learning. We propose a system that transforms an expressive drum grid, a time-aligned MIDI representation with microtiming and velocit...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Overall, our results establish codec-token prediction as an effective route for drum grid-to-audio generation and provide practical insights into selecting audio tokenizers for percussive synthesis. A pub...
WHY NOW
Audio Synthesis moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
A system synthesizes realistic drum audio from expressive drum grids using neural audio codecs and Transformer models. We propose a system that transforms an expressive drum grid, a time-aligned MIDI representation with microtiming and velocity information, into drum audio by predicting discrete codes of a neural audio codec.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Generating realistic drum audio directly from symbolic representations is a challenging task at the intersection of music perception and machine learning. We propose a system that transforms an expressive drum grid, a time-aligned MIDI representation with microtiming and velocity information, into drum audio by predicting discrete codes of a neural audio codec.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Overall, our results establish codec-token prediction as an effective route for drum grid-to-audio generation and provide practical insights into selecting audio tokenizers for percussive synthesis. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Audio Synthesis moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A system synthesizes realistic drum audio from expressive drum grids using neural audio codecs and Transformer models.
Segment
Audio Synthesis
Adoption evidence
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Commercial read
7.0/10 public viability
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Artifact maturity
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fresh
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Technical feasibility
partial
Current read
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People
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ARTIFACTS
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