Audio-to-Score Transcription using Pre-trained Features, Data Augmentation, and the New SheetSage-A2S Dataset

Alexandre D'Hooge; Eoin Cummins; Yaolong Ju; Zhongyi Huang; Zhuoro Mo

REVIEW 4 major objections 5 minor 39 references

Audio-to-Score Transcription using Pre-trained Features, Data Augmentation, and the New SheetSage-A2S Dataset

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A score-predicting transformer that uses a frozen pretrained music encoder and pitch/time augmentation transcribes real popular-music audio into lead sheets at a 20.92% symbol error rate, and cuts classical string-quartet error from 15.3%…

desk verdict A genuinely useful popular-music A2S dataset and a competent model paper; the dataset is the real contribution, and the headline popular benchmark needs more validation before being treated as canonical. read the letter →

arxiv 2608.06165 v1 pith:BBDIM4HE submitted 2026-08-06 cs.SD cs.AIcs.MM

Eoin Cummins , Zhongyi Huang , Alexandre D'Hooge , Zhuoro Mo , Yaolong Ju This is my paper

classification cs.SDcs.AIcs.MM

keywords audio-to-scoretranscriptionsymbolerrorrateleadsheetsSheetSage-A2SMuQdataaugmentation**kernpopularmusic

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper argues that audio-to-score transcription—turning a recording directly into a human-readable musical score—can work on real commercial popular music, not just synthetic classical corpora, if the model is built on a pretrained music-audio representation and trained with pitch-shifting and time-stretching augmentation. To test this, the authors introduce SheetSage-A2S, a dataset of 61 hours of audio paired with 9,468 **kern lead-sheet annotations from 6,066 songs, the first popular-music resource of its kind. On the classical Quartets collection their model reaches a 4.98% symbol error rate (the minimum edits needed to turn the predicted token sequence into the reference), down from the 15.3% reported for the prior transformer approach; on SheetSage-A2S it sets a first benchmark at 20.92%. If the claim holds, the remaining bottleneck is less the model architecture and more the consistency of user-generated annotations and the reliability of the octave-inference step.

What carries the argument

The central object that carries the argument is MuQ, a self-supervised music-audio feature extractor trained with mel residual vector quantization on a large music corpus; it is kept frozen and its 1024-dimensional output is projected and normalised down to 256 dimensions before serving as cross-attention memory for an autoregressive transformer decoder. The decoder itself is widened (feedforward 256 to 1024) and switched to pre-norm stabilisation, and training adds six pitch-shifted variants per clip (each with a randomly sampled time stretch between 0.9 and 1.1), transposing the symbolic target to match. On the dataset side the load-bearing step is octave inference: vocal stems are separated, a vocal pitch estimator produces per-frame pitch probabilities, twelve octave/start-time hypotheses are scored by normalised probability mass inside note masks, and clips scoring below 0.2 are discarded.

What would settle it

Run an independent octave check on a random sample of the 9,468 retained SheetSage-A2S clips: have trained musicians annotate the sung melody's absolute octave from the audio alone, and compare with the octave assigned by the 12-hypothesis confidence procedure. If the two disagree on more than a few percent of clips that passed the 0.2 threshold, the 20.92% SER benchmark is contaminated by annotation error and would need to be recomputed on a re-cleaned subset.

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Extended reading notes

Core claim

The paper's central claim is that direct audio-to-score transcription becomes substantially more accurate when a frozen self-supervised music representation replaces a learned-from-scratch spectrogram encoder, and when pitch/time augmentation is used during training. On the paper's own numbers, the recipe reduces Quartets SER from 15.3% to 4.98%, with the largest single gain on the popular-music dataset coming from the pretrained encoder alone (53.7% to 25.39% before augmentation). The new dataset, SheetSage-A2S, is presented as the first popular-music A2S benchmark, built from real recordings and user-generated lead sheets, and the reported 20.92% SER is offered as a strong baseline for future work rather than a solved problem.

Load-bearing premise

The dataset's ground-truth scores stand or fall on the octave-inference step: for each clip the absolute octave is taken from the highest-scoring of 12 pitch hypotheses, and clips with confidence below 0.2 are discarded—so if that threshold admits a substantial share of wrong-octave clips, the reported 20.92% popular-music SER measures the annotation pipeline as much as the model.

Editorial extensions

If this is right

Future A2S work on popular music gets a fixed comparison point: 20.92% SER on SheetSage-A2S, with separate melody (38.62%) and chord (22.28%) scores.
The classical-music result transfers: the same model cuts Quartets SER from 15.3% to 4.98%, a 67.5% relative reduction.
Because MuQ is frozen, the trainable parts are the projection layer, positional encodings, and decoder, so the approach is relatively cheap to reproduce.
Word-level tokenisation wins over mid-level and character-level on this task, so sequence-length reduction is worth more here than the bijective token-to-symbol property of mid-level schemes.
The per-spine numbers indicate chords are the harder subproblem on lead sheets, pointing future work toward chord-specific modelling or cleaned chord annotations.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Because MuQ was pretrained largely on popular music, a portion of the SheetSage-A2S gain may reflect overlap with the pretraining corpus; the paper acknowledges this is hard to quantify, and a clean test is to split the test set into songs likely and unlikely to be in that corpus and compare SER on each.
Augmentation and the frozen-encoder recipe should transfer to other A2S targets such as instrumental lead melodies or full polyphonic scores; the paper's own future-work section points in that direction, where octave inference would need to rely on a different pitch source than vocal stems.
The confidence threshold of 0.2 was calibrated on manual inspection of about 150 clips; if the dataset grows, publishing per-clip confidence scores would let downstream users weight or filter samples and re-estimate the benchmark without re-listening.
Since the model's timing errors are mostly within an eighth note and preserve overall phrase length, a musically-weighted evaluation that tolerates small offset shifts would likely report a smaller effective error than raw SER; the paper notes that existing musically-weighted metrics do not support lead-sheet formats.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Here's the short version: this is a dataset paper first and a modeling paper second. The SheetSage-A2S dataset is the real contribution—the first A2S resource built from real commercial recordings of popular music, with 9,468 clips and 61 hours of audio paired with **kern lead sheets. That fills a genuine gap. The model side is competent but not conceptually new: they take the [1] transformer decoder, replace the CNN encoder with frozen MuQ features, widen the feedforward layer, switch to pre-norm, and add pitch/time-stretch augmentation. The ablations in Table 2 are clean and every addition helps on both datasets. I believe the core methodological claim.

The soft spots are real but not fatal. The load-bearing one is the octave inference. Absolute octaves in the ground truth come from RMVPE on vocal-separated audio, with 12 octave/start-offset hypotheses scored by pitch probability mass, and a confidence threshold of 0.2 calibrated by manually inspecting around 150 clips. That is a thin basis for the 6,842 discarded clips, and RMVPE was tested on synthetic instruments, so using it on real commercial recordings is an extrapolation. If even a few percent of retained clips have wrong octaves, the training targets and the 20.92% SER on SheetSage-A2S are off. The limitation does not ruin the dataset—relative pitches, chords, and timing are still useful—but the popular benchmark should be read as provisional, not definitive.

Second, the numbers are single-run with no error bars. With a 7x training augmentation and early stopping, seed variance could easily shift the 4.98% SER on Quartets. The comparison to [1] is also not perfectly controlled: the original used Adam with lr 1e-4 and batch size 1, while their models use AdamW, lr 5e-5, and batch 8. The paper says the baseline row is the published 15.3, so the ablation is internally consistent, but the reported improvement bundles optimizer and batch-size changes with the architecture changes.

The potential overlap between SheetSage-A2S and MuQ's MSD pretraining is acknowledged in the paper. The authors argue the Quartets gains show the features generalize; that is fair, but the popular-music gain could be inflated by test-song overlap with pretraining. They should try to quantify that overlap.

Who is this for? MIR researchers working on A2S, lead sheets, or dataset construction. The released data, features, and code make it directly usable. Citation pattern looks fair—prior A2S work is cited, and the SheetSage source is credited. I would send this to peer review, with the request that the authors add variance estimates, a more substantial octave-validation study, and full baseline training details before the benchmark numbers are treated as canonical.

Referee Report

4 major / 5 minor

Summary. The paper introduces SheetSage-A2S, a new popular-music audio-to-score dataset of 9,468 clips (61 hours) paired with **kern lead-sheet annotations derived from SheetSage JSON and YouTube audio, together with an octave-inference pipeline based on RMVPE pitch probabilities. The authors also propose an A2S model that augments the transformer decoder of Alfaro-Contreras et al. with a larger pre-norm decoder, a frozen MuQ encoder, and pitch-shift/time-stretch data augmentation. They report a 4.98% symbol error rate on the Quartets classical corpus versus 15.3% for the prior state of the art, and 20.92% SER on SheetSage-A2S, which they present as a first benchmark for popular-music A2S. An ablation table shows progressive gains from each modification, and a tokenisation study compares character-, mid-, and word-level schemes.

Significance. If the dataset and its ground-truth construction are reliable, SheetSage-A2S is a valuable contribution: it is the first large-scale A2S resource for popular music built from real commercial recordings, it is released with annotations and code, and it addresses a clear gap in the field. The architectural choices (using a frozen pretrained music encoder and simple data augmentation) are well motivated, and the ablation ordering in Table 2 is internally consistent. The paper also provides per-spine melody/chord SER, which is useful for error analysis. However, the central quantitative claims rest on two points that need strengthening: the validation of the octave-inference step that determines the absolute pitches in the new dataset, and the absence of any statistical uncertainty around the reported SER values.

major comments (4)

[Section 2.2, Octave Inference] The quality check for the ground-truth construction is not sufficiently documented. The retention threshold C > 0.2 is justified by manual inspection of "around 150 clips" and the claim that no incorrect octave was found above the threshold, yet the retained set contains 9,468 clips and the rejected set 6,842 clips. No confidence distribution, no margin between the best and second-best hypothesis, and no independent verification on a held-out random sample of retained clips are reported. Since Figure 4b shows that 98.66% of retained clips are assigned to octaves 3 or 4, a small systematic bias in the octave-inference prior would directly corrupt training targets and the 20.92% SER benchmark. Please add a quantitative validation of retained-set octaves, such as manual verification of a random sample, agreement with an independent pitch tracker, and a report of the confidence margins.
[Table 2 and Section 5.1] All SER values in Table 2 and Table 3 are point estimates from single training runs, with no repeated seeds, confidence intervals, or significance tests. The claims that the method "significantly outperforms" the prior 15.3% baseline and that each ablation step helps rest entirely on these point estimates. Because neural training is stochastic and the augmentation expansion changes the training data distribution, please provide variance estimates (e.g., 3-5 seeds, or bootstrap over test clips) for the final results and at least the key ablation rows.
[Section 4.2, Training setup and Table 2] The comparison with the baseline of [1] is not fully specified. The paper states that the training setup differs from the original (AdamW, weight decay 1e-3, lr 5e-5, batch size 8 versus Adam, no weight decay, lr 1e-4, batch size 1), but Table 2 lists "Baseline [1]" as 15.3 on Quartets. Please state explicitly whether this number was reproduced under the new optimizer or copied from [1]. If it was copied, the 4.98% result conflates architectural changes with training-scheme changes, and a retrained baseline under the new scheme is needed for a fair ablation.
[Section 5.2, Encoder and augmentation] The paper acknowledges that SheetSage-A2S songs may overlap with the MSD corpus used to pretrain MuQ, but it does not quantify this overlap. Because the popular-music benchmark is a central contribution, this overlap could inflate both the apparent benefit of MuQ and the absolute 20.92% SER figure. Please attempt to quantify the overlap (e.g., via metadata matching or audio fingerprints) and report results separately for likely-overlapping and non-overlapping test clips, or provide a justification for why overlap cannot affect the frozen-feature evaluation.

minor comments (5)

[Section 2.2, Hypothesis selection] The phrase "we discard clips with confidence C = argmax_H C(H) below a threshold of 0.2" is notationally confused: the scalar confidence should be max_H C(H), not the argmax. Please correct the notation.
[Section 3.1 and Section 5.2] The "1024-PreNorm baseline" changes two architectural factors simultaneously (feedforward dimension and normalisation order). The statement in Section 5.2 that the improvement is "primarily" due to the increased feedforward dimension is not supported by an ablation isolating these two changes. Please add such an ablation or soften the attribution.
[Figure 4(a)] The histogram is truncated at 100 seconds while the text states that 12 clips exceed 100 seconds. Please report the maximum clip duration and consider showing the tail explicitly (e.g., with an inset).
[Section 4.1] The artist-stratified split is described for SheetSage-A2S, but since multiple clips can come from one song, please confirm explicitly that all clips of a given song are kept within the same split.
[Section 6.2] The sentence "Measures 5 to 7 from Love Live - Bokutachi wa Hitotsu no Hikari. 12 It demonstrates..." has an ambiguous footnote marker placement; please clarify the footnote or citation formatting.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: reported SERs are measured on held-out test sets and the model does not use RMVPE at inference; the octave-inference step is a data-cleaning procedure, not a derived prediction.

full rationale

The paper's derivation chain is self-contained. The core claims are (i) a 4.98% SER on the existing synthetic Quartets benchmark against the 15.3% reported baseline [1], and (ii) a 20.92% SER on the new SheetSage-A2S dataset. Both numbers are measured on held-out test splits with greedy decoding; they are not derived from the input by construction. The new dataset is built from SheetSage JSON annotations and YouTube audio, with absolute octaves inferred by an RMVPE-based hypothesis-selection procedure. This is a label-generation step, and the paper explicitly checks it on ~150 clips. The downstream A2S model does not use RMVPE or the octave-inference confidence at inference time, so the SER is not fitted to, nor a restatement of, the octave-inference output. The paper's own limitations (Section 6.1 and the Section 2.2 footnote about pitch tools tested on synthetic instruments) concern label noise and generalisation risk, not circularity. All references are to external prior work; there are no load-bearing self-citations, uniqueness theorems, or ansatz smuggling. The ablation study in Table 2 provides independent evidence that each architectural modification contributes to the measured SER. No equation or definition reduces a predicted quantity to an input parameter, so no circular step is present.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The central claims rest on the quality of the SheetSage annotations, the reliability of the external pitch and separation models used to build the ground truth, and the clean transfer of MuQ features; these are assumptions rather than results demonstrated in this paper.

free parameters (6)

Octave-inference confidence threshold = 0.2
Clips below this normalized pitch probability mass are discarded; the value was set by manual inspection of about 150 clips (Section 2.2).
Silence RMS threshold = 0.0005
Used to filter clips with no singing vocal energy (Section 2.2).
Pitch-shift augmentation range = {-3,-2,-1,1,2,3} semitones
Chosen to provide variety while preserving plausibility (Section 3.2).
Time-stretch augmentation factors = {0.9,0.95,1.05,1.1}
Hand-picked set; no sensitivity study is reported (Section 3.2).
Label smoothing = 0.1
Applied to SheetSage-A2S training; reported to give a small SER decrease (Section 4.2).
Rhythm quantization maximum denominator = 96
Rational fractions with a maximum denominator of 96 to handle grace notes (Section 2.3).

assumptions (6)

domain assumption SheetSage user annotations are a valid ground truth for melody and chords in popular music.
All scores derive from the HookTheory/SheetSage annotations of [10]; the paper itself documents their inconsistencies in Section 6.1.
domain assumption madmom beat and downbeat tracking reliably maps beat-indexed note onsets to audio timestamps.
Used to compute the alignment function A in Section 2.1; cited from [5,6] with no independent evaluation in this paper.
domain assumption RMVPE's normalized pitch probability correctly identifies the absolute octave when the confidence score exceeds 0.2.
Octave inference in Section 2.2 selects the max-confidence hypothesis among 12; correctness was checked on only about 150 clips.
domain assumption Mel-RoFormer vocal separation provides a vocal stem clean enough for RMVPE to estimate melody pitch.
Vocal stem extraction is a preprocessing step in Section 2.2; no evaluation of separation quality is reported.
domain assumption MuQ features learned on the Million Song Dataset transfer to A2S and are not biased by train-test overlap on SheetSage-A2S.
The paper acknowledges possible overlap in Section 5.2 and offers no quantification.
domain assumption Levenshtein SER on word-level tokens is a meaningful measure of musical score quality.
Used as the primary metric following [1]; the paper notes per-spine SER can exceed 100%.

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Cite this review

Pith. "Pith review of Audio-to-Score Transcription using Pre-trained Features, Data Augmentation, and the New SheetSage-A2S Dataset." pith.science (2026). https://pith.science/paper/BBDIM4HE

@misc{pith2026260806165,
  author       = {Pith},
  title        = {Pith review of: Audio-to-Score Transcription using Pre-trained Features, Data Augmentation, and the New SheetSage-A2S Dataset},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BBDIM4HE}},
  note         = {Machine review of arXiv:2608.06165}
}

read the original abstract

Existing audio-to-score (A2S) systems primarily focus on classical music, and the application to popular music remains underexplored. This paper first presents the new SheetSage-A2S Dataset, which includes 61 hours of audio with \texttt{**kern} score encodings for 9,468 clips originating from 6,066 unique songs, the first of its kind to facilitate A2S research for popular music. Additionally, we improve on existing A2S approaches by using data augmentation and MuQ, a pretrained feature-extraction model for music audio, to enhance generalisation abilities and extract meaningful audio features. Results show that the proposed A2S model achieves 4.98\% symbol error rate (SER) on the Quartets collection for classical music, which significantly outperforms the 15.3\% SER from the existing state-of-the-art \cite{alfaro-contrerasTransformer2024}. Additionally, our model achieves 20.92\% SER on the SheetSage-A2S dataset for popular music, serving as a strong benchmark for future research. The dataset, model, and code are made publicly available at: https://github.com/Multimodal-Music-Research-Lab/SheetSage2Kern_model.

Figures

Figures reproduced from arXiv: 2608.06165 by the authors.

**Figure 2.** Overview of the SheetSage-A2S dataset creation pipeline, with three main steps that are introduced in detail at [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Summary of the json to **kern conversion pipeline. Numbers denote the four main processing steps described in Section 2.3. Combining six octave shifts with two start time offsets gives 12 hypotheses per clip. Hypothesis selection. We score each hypothesis by the probability mass from 𝑃 that falls inside its mask, normalised by total note duration: 𝐶(𝐻) = Í (𝑡, 𝑝) ∈ 𝐻 𝑃 (𝑡, 𝑝) Í 𝑛∈𝑁 [PITH_FULL_IMAGE:figures/full_fi… view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: SheetSage-A2S dataset statistics. (a) Clip duration, [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: Comparison of our proposed architecture against [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

**Figure 6.** Figure 6: Measures 5 to 7 from Love Live - Bokutachi wa Hitotsu no Hikari.12 It demonstrates that our best model can have trouble determining exact note-rest boundaries and non-triad chord labels. and others only annotate when the pitch changes. Furthermore, singers often emplo…

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

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