arxiv: 2605.10281 · v1 · submitted 2026-05-11 · 💻 cs.SD · cs.AI

Recognition: no theorem link

Drum Synthesis from Expressive Drum Grids via Neural Audio Codecs

Konstantinos Soiledis , Maximos Kaliakatsos-Papakostas , Dimos Makris , Konstantinos Tsamis

Authors on Pith no claims yet

Pith reviewed 2026-05-12 03:10 UTC · model grok-4.3

classification 💻 cs.SD cs.AI

keywords drum synthesisneural audio codecsTransformerMIDI to audiopercussive synthesisexpressive gridscodec tokensE-GMD dataset

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The pith

A Transformer predicts neural audio codec tokens from expressive MIDI drum grids to generate realistic drum audio.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that mapping expressive drum grids to sequences of discrete tokens from pre-trained neural audio codecs allows a Transformer to produce drum audio via the codec decoder. It tests this with EnCodec, DAC, and X-Codec on the Expanded Groove MIDI Dataset of human performances and measures output fidelity and alignment through objective metrics. A reader would care because the method reuses existing audio tokenizers instead of training audio generators from scratch, offering a practical route from symbolic percussion input to waveform output. The results indicate that codec choice influences synthesis quality for percussive material.

Core claim

Training a Transformer to predict discrete codes from neural audio codecs given time-aligned MIDI drum grids with microtiming and velocity information produces audio that can be decoded into waveforms, establishing codec-token prediction as an effective route for drum grid-to-audio generation while highlighting differences across EnCodec, DAC, and X-Codec tokenizers.

What carries the argument

Transformer model that maps expressive drum grid sequences to neural audio codec token sequences for decoding into audio waveforms.

If this is right

Different neural audio codecs produce measurably different drum synthesis quality when used as token targets.
Objective metrics quantify how well the generated audio aligns with the input grid's microtiming and dynamics.
The approach applies directly to the Expanded Groove MIDI Dataset containing paired MIDI and audio from human drummers.
Microtiming and velocity data in the grids are transferred to audio through the predicted token sequences.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same token-prediction strategy could be tested on other percussive instruments or full drum kits with additional sound sources.
Insights on tokenizer selection may help choose representations for related tasks such as groove continuation or drum accompaniment generation.
Adding subjective listening evaluations would provide a complementary check on whether objective scores match perceived musical quality.

Load-bearing premise

The discrete tokens from pre-trained neural audio codecs retain enough percussive detail and timing information to support accurate prediction from MIDI grids.

What would settle it

Generated audio that shows measurable timing drift or loss of velocity nuance relative to the original human performances in the E-GMD dataset would falsify the claim that token prediction preserves musical fidelity.

Figures

Figures reproduced from arXiv: 2605.10281 by Dimos Makris, Konstantinos Soiledis, Konstantinos Tsamis, Maximos Kaliakatsos-Papakostas.

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper gives a working Transformer pipeline that predicts tokens from EnCodec, DAC or X-Codec to turn expressive drum grids into audio, tested on E-GMD.

read the letter

The main takeaway is a straightforward system that maps time-aligned MIDI grids carrying velocity and microtiming into discrete codec tokens via a Transformer, then decodes them with a frozen pre-trained decoder. It runs the same setup on three different neural codecs and reports objective scores on the Expanded Groove MIDI Dataset. That specific combination—expressive grids to codec tokens for drums—is not in the earlier literature the abstract cites, so the work adds a concrete application rather than another generic audio model. The choice to keep the codecs frozen is sensible; it avoids retraining large audio models and lets the authors focus on the mapping step. Using a dataset that already pairs MIDI and audio also removes one common source of mismatch. The design directly tests whether the quantized tokens can carry enough percussive detail and timing to be useful. No obvious circularity or self-referential fitting shows up in the argument. The soft spots are mostly about evaluation depth. Objective metrics are reported, but without the actual numbers, error bars, or ablation tables it is hard to judge how large the differences between codecs really are or whether they beat simpler baselines. Perceptual listening tests would help confirm that the objective scores track musical quality for drums. The paper stays within its stated scope and does not overclaim generality. It is aimed at researchers and engineers working on symbolic-to-audio conversion for percussion, especially those already using neural codecs in production pipelines. The experiments are reproducible in principle once the code and hyperparameters are released. I would send it to peer review; the core idea is cleanly executed and the codec comparison supplies practical information even if the gains turn out to be modest.

Referee Report

0 major / 3 minor

Summary. The paper proposes a Transformer-based model that maps expressive drum grids (time-aligned MIDI with microtiming and velocity) to sequences of discrete tokens from pre-trained neural audio codecs (EnCodec, DAC, X-Codec). These predicted tokens are decoded to waveform audio using the frozen codec decoder. The system is trained and evaluated on the Expanded Groove MIDI Dataset (E-GMD) using objective metrics for fidelity and musical alignment. The authors conclude that codec-token prediction is an effective route for drum grid-to-audio generation and yields practical insights into selecting audio tokenizers for percussive synthesis.

Significance. If the empirical results hold, the work demonstrates a modular, efficient approach to symbolic-to-audio drum synthesis that leverages existing pre-trained codecs rather than training end-to-end waveform models. This could be useful for music generation tasks where timing precision matters. The comparative evaluation across three codecs on real human performances is a positive aspect. The stress-test concern regarding preservation of percussive transients in tokens does not invalidate the central claim, as the grid-to-token prediction design directly tests the route; however, the adequacy of objective metrics as proxies for musical quality remains a point for further validation.

minor comments (3)

The abstract states that objective metrics are used for evaluation but provides no numerical values, baselines, error bars, or specific findings from the EnCodec/DAC/X-Codec comparisons. The full manuscript should report these quantitative results explicitly (e.g., in §4 or Table 1) to allow readers to assess the effectiveness claim.
Clarify the input representation of the expressive drum grid to the Transformer (e.g., how microtiming offsets and velocity values are tokenized or embedded). This detail is needed in the method section to understand how timing information is preserved through the mapping.
The paper should discuss potential limitations of relying solely on objective metrics for percussive audio, such as their correlation with human perception of timing and timbre; a brief note on this would strengthen the evaluation.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary of our work, the favorable assessment of the modular codec-based approach, and the recommendation for minor revision. No specific major comments were provided in the report.

Circularity Check

0 steps flagged

No significant circularity; empirical pipeline uses external pre-trained codecs and independent dataset

full rationale

The paper presents an empirical ML system that maps expressive drum grids to discrete tokens from pre-trained neural audio codecs (EnCodec, DAC, X-Codec) using a Transformer, then decodes via the frozen codec decoder. Training and evaluation occur on the external E-GMD dataset with objective metrics. No equations, derivations, or self-citations appear in the abstract or described structure that reduce the central claim to fitted inputs by construction, self-definition, or load-bearing prior work by the same authors. The approach tests a practical route without renaming known results or smuggling ansatzes; the token prediction is a learned mapping, not a tautological restatement of inputs. This is a standard self-contained empirical contribution.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that pre-trained neural audio codecs already encode percussive sounds adequately in discrete tokens and that a Transformer can learn the grid-to-token mapping from paired MIDI-audio data. Many training and architectural choices remain unspecified in the abstract.

free parameters (2)

Transformer hyperparameters
Number of layers, attention heads, embedding dimension, and sequence length chosen during model design and training.
Training schedule parameters
Learning rate, batch size, number of epochs, and optimizer settings fitted to the E-GMD data.

axioms (1)

domain assumption Pre-trained neural audio codecs (EnCodec, DAC, X-Codec) produce discrete tokens that preserve sufficient timing and timbre information for drum sounds.
Invoked by the decision to use these codecs as the target representation without retraining them on drum data.

pith-pipeline@v0.9.0 · 5496 in / 1413 out tokens · 54742 ms · 2026-05-12T03:10:11.219065+00:00 · methodology

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Seconds-Aligned PCA-DAC Latent Diffusion for Symbolic-to-Audio Drum Rendering
cs.SD 2026-05 unverdicted novelty 6.0

Sec2Drum-DAC renders drum audio from symbolic inputs via diffusion on PCA-reduced DAC latents, improving spectral and transient metrics over regression baselines on 1733 held-out windows.

Reference graph

Works this paper leans on

17 extracted references · 17 canonical work pages · cited by 1 Pith paper · 2 internal anchors

[1]

Audiolm: a language modeling approach to audio generation

Z. Borsoset al., “AudioLM: a language modeling approach to audio generation,”arXiv preprint arXiv:2209.03143, 2023, doi: 10.48550/arXiv.2209.03143

work page doi:10.48550/arxiv.2209.03143 2023
[2]

MusicLM: Generating Music From Text

A. Agostinelliet al., “MusicLM: generating music from text,”arXiv preprint arXiv:2301.11325, 2023, doi: 10.48550/arXiv.2301.11325

work page internal anchor Pith review doi:10.48550/arxiv.2301.11325 2023
[3]

Improving perceptual quality of drum transcription with the expanded groove midi dataset,

L. Callender, C. Hawthorne, and J. Engel, “Improving perceptual quality of drum transcription with the expanded groove MIDI dataset,”arXiv preprint arXiv:2004.00188, 2020, doi: 10.48550/arXiv.2004.00188

work page doi:10.48550/arxiv.2004.00188 2004
[4]

High Fidelity Neural Audio Compression

A. D ´efossez, J. Copet, G. Synnaeve, and Y . Adi, “High fidelity neu- ral audio compression,”arXiv preprint arXiv:2210.13438, 2022, doi: 10.48550/arXiv.2210.13438

work page internal anchor Pith review doi:10.48550/arxiv.2210.13438 2022
[5]

Codec does matter: Exploring the semantic shortcoming of codec for audio language model,

Z. Yeet al., “Codec does matter: exploring the semantic shortcoming of codec for audio language model,”arXiv preprint arXiv:2408.17175, 2024, doi: 10.48550/arXiv.2408.17175

work page doi:10.48550/arxiv.2408.17175 2024
[6]

High- fidelity audio compression with improved RVQGAN,

R. Kumar, P. Seetharaman, A. Luebs, I. Kumar, and K. Kumar, “High- fidelity audio compression with improved RVQGAN,”arXiv preprint arXiv:2306.06546, 2023, doi: 10.48550/arXiv.2306.06546

work page doi:10.48550/arxiv.2306.06546 2023
[7]

DrumGAN: Synthesis of drum sounds with timbral feature conditioning using generative adversarial networks,

J. Nistal, S. Lattner, and G. Richard, “DrumGAN: Synthesis of drum sounds with timbral feature conditioning using generative adversarial networks,” inProc. ISMIR, 2020. doi: 10.48550/arXiv.2008.12073

work page doi:10.48550/arxiv.2008.12073 2020
[8]

Learn- ing to groove with inverse sequence transformations,

J. Gillick, A. Roberts, J. Engel, D. Eck, and D. Bamman, “Learn- ing to groove with inverse sequence transformations,”arXiv preprint arXiv:1905.06118, 2019, doi: 10.48550/arXiv.1905.06118

work page doi:10.48550/arxiv.1905.06118 1905
[9]

M., Simon, I., Sheahan, H., Zeghidour, N., Alayrac, J., Carreira, J., and Engel, J

C. Hawthorneet al., “Multi-instrument music synthesis with spectro- gram diffusion,” inProc. ISMIR, 2022, doi: 10.48550/arXiv.2206.05408

work page doi:10.48550/arxiv.2206.05408 2022
[10]

CRASH: Raw audio score-based generative modeling for controllable high-resolution drum sound synthesis,

S. Rouard and G. Hadjeres, “CRASH: Raw audio score-based generative modeling for controllable high-resolution drum sound synthesis,” in Proc. ISMIR, 2021, doi: 10.48550/arXiv.2106.07431

work page doi:10.48550/arxiv.2106.07431 2021
[11]

A Comparison table: axis definitions and per-method justifications We expand on Table

N. Zeghidour, A. Luebs, A. Omran, J. Skoglund, and M. Tagliasac- chi, “SoundStream: An end-to-end neural audio codec,”IEEE/ACM Trans. Audio Speech Lang. Process., vol. 30, pp. 495–507, 2022, doi: 10.1109/TASLP.2021.3129994

work page doi:10.1109/taslp.2021.3129994 2022
[12]

MIDI-V ALLE: Improving expressive piano performance synthesis through neural codec language modelling,

J. Tanget al., “MIDI-V ALLE: Improving expressive piano performance synthesis through neural codec language modelling,”arXiv preprint arXiv:2507.08530, 2025

work page arXiv 2025
[13]

STAGE: Stemmed accompaniment generation through prefix-based conditioning,

G. Stranoet al., “STAGE: Stemmed accompaniment generation through prefix-based conditioning,”arXiv preprint arXiv:2504.05690, 2025

work page arXiv 2025
[14]

DARC: Drum accompaniment generation with fine-grained rhythm control

T. Brosnan, “DARC: Drum accompaniment generation with fine-grained rhythm control,”arXiv preprint arXiv:2601.02357, 2026

work page arXiv 2026
[15]

The Rhythm In Anything: Audio-Prompted Drums Generation with Masked Language Modeling (TRIA),

P. O’Reillyet al., “The Rhythm In Anything: Audio-Prompted Drums Generation with Masked Language Modeling (TRIA),”arXiv preprint arXiv:2509.15625, 2025

work page arXiv 2025
[16]

Fr ´echet Audio Distance: A Reference-Free Metric for Evaluating Music Enhancement Algorithms,

K. Kilgour, M. Zuluaga, D. Roblek, and M. Sharifi, “Fr ´echet Audio Distance: A Reference-Free Metric for Evaluating Music Enhancement Algorithms,” inProc. Interspeech, 2019

work page 2019
[17]

Adapting Fr ´echet Audio Distance for Generative Music Evaluation,

A. Gui, H. Gamper, S. Braun, and D. Emmanouilidou, “Adapting Fr ´echet Audio Distance for Generative Music Evaluation,” inProc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), 2024

work page 2024