{"id":"9c465c99-c48f-4768-95de-ffb2214fd475","arxiv_id":"2608.03050","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":4,"one_line_summary":"A cross-modal model extracts performance style from audio and uses it to generate matching piano arrangements from lead sheets, with style transfer and audio-to-MIDI retrieval.","lead":"This paper builds a music AI that listens to an audio clip, pulls out the style of the performance, and writes a piano arrangement of a given melody that matches that style. It chains two large pre-trained music models with a lightweight connector, avoiding the cost of retraining the big models.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Retrieval evaluation in §5.2 may sample from training data; if so, Acc@1≈71% reflects memorization rather than a transferable style representation, undercutting the key evidence for cross-modal style learning.","rationale":"The paper's central claim is that the Q-Former extracts a transferable style representation from audio that improves stylistically faithful piano arrangement. The most direct, controlled evidence for this representation is the audio-to-MIDI retrieval experiment in §5.2, which is presented as a general-purpose test of cross-modal alignment independent of the generative decoder. If the retrieval test set is not held out, the reported Acc@1 of 71.4% (vs. 3.4% for CLaMP3) can be explained by the model matching training pairs it has already seen, not by a learned notion of style. This does not necessarily refute the generation results, but it removes the cleanest support for the 'style representation' claim, leaving the argument dependent on in-distribution GPC/VCC and a small subjective study. The reader identified the retrieval split as an uncertainty, so the agreement is partial; the reader's primary weakest assumption was the data-pairing local-consistency assumption. I weight the retrieval split as more load-bearing because it is a discrete, checkable potential flaw in a key experiment, whereas the data-pairing assumption is explicitly acknowledged and empirically supported by the system's operation. If the authors confirm the split is held out and the transposition condition is correctly implemented, the concern is resolved and the conditional acceptance is appropriate. If not, the paper should be revised to either provide a valid held-out retrieval evaluation or temper the claim that the Q-Former is a general-purpose cross-modal representation learner.","tokens_in":18952,"tokens_out":5507,"duration_ms":53440,"concrete_test":"Check the data-split indices: verify that all 128 audio clips and 128 MIDI segments in the §5.2 retrieval pool are from the POP909 and PIAST test splits (and not from training/validation). If they are not, recompute Table 3 using only held-out songs, with the same 10-run resampling; if Acc@1 drops substantially (e.g., below 30%), the retrieval evidence for a transferable style representation does not hold. Also confirm that the random-transposition condition transposes the MIDI candidates only and that the ground-truth pair remains unique.","verdict_should_be":"UNCHANGED","load_bearing_attack":"In §5.2, the audio-to-MIDI retrieval test constructs 128 pairs 'randomly sampled from PIAST and POP909 (64 pairs each)' without stating that these are drawn from the held-out test splits. The rest of the paper carefully distinguishes training/validation/test splits (§4.1), so the omission is conspicuous. If the retrieval pool includes training songs, the model has been optimized to map those exact audio clips to their paired MIDI via the contrastive and matching losses in Stage-I; high Acc@1 (71.4%) and Rank (2.1) would then be expected from memorization, and would not demonstrate that the Q-Former captures transferable style. This matters because the retrieval task is the paper's cleanest evidence that the Q-Former learns a general-purpose cross-modal style representation: generation metrics (GPC/VCC) are only computed on the in-distribution POP909 set with coarse onset/velocity vectors, and TA on OOD sets captures only tempo. If the retrieval evidence is invalid, the claim that the Q-Former extracts 'implicit music style' rests on weaker, coarser metrics and on a small subjective study (21 participants, 3 pieces each). The paper should state explicitly whether the 128 pairs are disjoint from training and validation, and if not, re-run Table 3 on the held-out test splits.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes a two-stage cross-modal framework for piano arrangement in which a Q-Former is trained to extract implicit style representations from a frozen audio language model (MusicGen) and to condition a frozen symbolic language model (MuseCoco) for generating piano performances from a lead sheet and a reference audio clip. Stage-I uses contrastive, matching, and audio-grounded generative losses on loosely aligned 10-second audio and 4-bar MIDI pairs; Stage-II fine-tunes a LoRA adapter while keeping the symbolic LM frozen. The paper evaluates the approach on piano cover generation, cross-modal style transfer, audio-to-MIDI retrieval, and a subjective listening test, reporting improvements in style coherence metrics over PiCoGen2 and an Audio-to-MIDI disentanglement baseline, as well as an ablation showing the value of Stage-I pre-training.","tokens_in":19218,"tokens_out":5639,"duration_ms":57253,"significance":"If the central claim holds, the paper offers a scalable way to transfer implicit musical style across audio and symbolic modalities without retraining large backbone models, which would be useful for controllable arrangement and style-conditioned generation. The architecture is described in detail, the two-stage training procedure is clear, and the authors provide a demo page and a candid limitations section. The main experimental strength is the combination of multiple objective metrics, an ablation study, and a user study. However, the cleanest evidence for a transferable style representation — the audio-to-MIDI retrieval experiment — currently has a potentially serious methodological gap, and the subjective evaluation relies on a small, non-blind sample-selection procedure. These issues need to be addressed before the paper's claims are fully supported.","major_comments":[{"comment":"The retrieval test set is described only as \"randomly sampled from PIAST and POP909 (64 pairs each)\" and does not state that these pairs are drawn from the held-out test splits defined in Section 4.1. Because the Q-Former is trained on PIAST and POP909 with contrastive and matching losses, including training pairs would make the reported Acc@1 of 71.4% reflect memorization rather than a transferable cross-modal representation. The paper must state explicitly that the 128 pairs are disjoint from the training and validation sets, and if they are not, Table 3 should be re-run on the held-out test splits.","section":"Section 5.2.1, Table 3"},{"comment":"Even if the retrieval pairs are held out, the task pairs each audio query with the MIDI segment from the same song, so high retrieval accuracy can be achieved through content cues such as melody contour, harmony, and rhythm rather than through style. The random-transposition condition removes absolute pitch as a cue, but it does not remove relative melodic and harmonic content. Therefore the claim that the Q-Former captures \"stylistic coherence beyond surface-level attributes\" is not established by this experiment. I recommend adding a retrieval condition in which positive pairs share style but not content (for example, different songs from the same genre or artist), or otherwise explicitly controlling for content similarity.","section":"Section 5.2.1, Table 3"},{"comment":"The subjective evaluation selects the \"best result from 3 generated samples\" for each model without a pre-registered or blinded selection criterion. With only 21 participants and 3 test pieces per participant, the significant preference for \"Ours\" in Coherence and Musicality is not robust evidence of superiority. I recommend either reporting results across all generated samples, using a blind and pre-defined selection rule, or presenting a sensitivity analysis with random sample selection.","section":"Section 4.4 and Appendix C.2"}],"minor_comments":[{"comment":"The manuscript contains an apparent rendering artifact — a long string of \"/uni...\" tokens — that should be removed or fixed before publication.","section":"Between Section 4.3 and Figure 3"},{"comment":"The text says the symbolic arrangements are tokenized in \"OctMIDI format [52]\" but reference [52] is the MusicBERT paper, which uses OctupleMIDI encoding; please check the terminology and citation.","section":"Appendix A.1"},{"comment":"The caption contains \"p-valuep < 0.05/6\", which should read \"p-value p < 0.05/6\" with appropriate spacing.","section":"Table 1 caption"},{"comment":"The study is described as \"double-blind,\" but participants are only blind to model identity while the authors select the best samples; please clarify the exact blinding procedure.","section":"Section 4.4"},{"comment":"The description of \"10 independent runs\" should clarify whether the same audio and MIDI segments may appear in multiple runs, and whether the 128 candidates are unique within each run.","section":"Section 5.2.1"}],"recommendation":"major_revision","confidential_remarks":"The retrieval evaluation issue is the main obstacle: the paper's own Section 4.1 carefully distinguishes train/validation/test splits, so the omission in Section 5.2.1 is conspicuous and should be treated as a correctness risk rather than a simple clarity issue. If the authors can re-run Table 3 on held-out splits and add a content-controlled retrieval condition, the paper's central claim would be substantially strengthened. The subjective evaluation issue is secondary but should also be addressed in revision."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Short version: this is a competent, useful empirical paper that is one missing sentence away from being trustworthy on its central representation claim. The new thing is using a BLIP-2-style Q-Former to connect a frozen audio LM to a frozen symbolic LM, trained first with contrastive/matching/generation objectives and then as a conditional generator. That is an extension of known machinery rather than a new paradigm, but it is a genuine new application, and the two-stage design is clearly described. The generative results are plausible: on POP909 the model leads on GPC/VCC/TA while trailing PCG2 on MCA/CA, and on Ballroom/GTZAN it generalizes better on content metrics. The demo of ragtime and bossa arrangements of The Sound of Music is convincing evidence that something style-related is being transferred, at least at the segment level. The authors also deserve credit for writing an honest limitations section (Appendix E) that flags the segment-level nature of the style representation and the smoothing at phrase boundaries.\n\nThe soft spot is the one flagged in the stress test: Section 5.2.1 says the 128 retrieval pairs are \"randomly sampled from PIAST and POP909\" but never says from the test splits. The rest of the paper is careful about train/validation/test, so this is conspicuous. If the pool includes training songs, Table 3's Acc@1 of 71% is exactly what a model optimized on those pairs would produce, and the retrieval experiment cannot support the claim that the Q-Former learns a transferable style representation. Table 4 has the same omission. This is a real flaw, but a fixable one: state the split and rerun if needed. The generative evidence does not collapse even if retrieval is partly memorization, but the \"general-purpose representation learner\" claim would be much weaker.\n\nSmaller issues: the subjective study uses best-of-3 selection, which flatters any model with variance; it has only 21 participants each rating 3 pieces; and no code is released. The GPC/VCC metrics are coarse onset/velocity vectors, and TA captures only tempo, so the style metrics are somewhat blunt. None of these are fatal; they should be disclosed and, ideally, tightened.\n\nBottom line: the central arrangement result holds up, the retrieval split needs to be fixed, and the paper deserves a serious referee. I would send it to review and ask for a revision that clarifies the retrieval split and releases code.","headline":"Worth a serious referee: the arrangement results are solid, but the retrieval experiment never says its pairs are held out, so the representation claim needs that clarified before it can be trusted.","tokens_in":19734,"tokens_out":2425,"would_cite":false,"duration_ms":24724,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"A lightweight connector between a frozen audio model and a frozen symbolic music model lets a lead sheet and a reference recording jointly control a piano arrangement, transferring the recording's groove, dynamics, and tempo into symbolic…","keywords":["cross-modal music representation","piano cover generation","implicit music style","style transfer","audio-to-MIDI retrieval","Q-Former","contrastive learning","symbolic music generation"],"falsifier":"Build a test set of 4-bar audio–MIDI pairs in which the style clearly changes at the midpoint (for example, straight eighths switching to swing, or a sudden tempo and texture break), generate covers conditioned on these clips, and count whether the second half's groove appears in the symbolic output. If the output reflects only an average of the two styles, the local-consistency assumption that carries the method gives way.","tokens_in":18735,"feed_emoji":"🎹","tokens_out":7088,"duration_ms":64512,"temperature":0.7,"pith_summary":"This paper argues that music style—the groove, dynamics, and tempo that make a performance feel a certain way—can be pulled out of a raw audio recording and stamped onto a symbolic piano arrangement, without retraining either a large audio model or a large symbolic music model. The authors connect the two frozen models with a Querying Transformer (Q-Former), a small trainable module that learns a compressed style representation from the audio model's hidden states and feeds it, together with a lead sheet, into the symbolic model. Training happens in two stages: first, contrastive and matching objectives align the audio and symbolic sides and a generative objective makes the style representation usable; second, a lightweight adapter conditions the symbolic model on the extracted style. If the claim holds, composers and arrangers could control not just what notes are played but how they are played by choosing a reference recording. The paper reports evidence on piano cover generation, style transfer, and audio-to-MIDI retrieval, including out-of-distribution genres.","feed_headline":"A lightweight connector carries a song's feel into piano MIDI covers","feed_subtitle":"A frozen audio model and a frozen music model share groove, dynamics, and tempo through a Q-Former.","key_machinery":"The load-bearing component is the Q-Former, a Querying Transformer: a Transformer encoder with $K=32$ learnable query vectors that cross-attend to the hidden states of a frozen audio language model while sharing self-attention with a stream of symbolic piano tokens. At test time only the audio stream remains, producing a style embedding $\\mathbf{Z}$ that conditions a frozen symbolic music language model through a linear projection and a LoRA adapter, alongside the lead sheet that supplies content. Stage-I trains the Q-Former with three masked objectives—audio-symbolic contrastive learning, audio-symbolic matching with hard negatives, and audio-grounded symbolic generation—each with a self-attention mask that controls how much the two modalities see each other. The design acts as a bottleneck: the queries pass style-related information forward while the lead sheet carries melody and harmony, so the large backbones never need to be retrained.","core_discovery":"On the paper's own terms, the central discovery is that a frozen audio language model and a frozen symbolic music language model can be bridged by a small Q-Former trained to extract style rather than content, and that the resulting system produces piano covers that are more stylistically coherent—matching the reference audio's grooving patterns, velocity contours, and tempo—than existing audio-to-symbolic arrangement methods. The style signal survives deliberately loose data pairing: 10-second audio clips are matched to 4-bar MIDI segments with random temporal shifts up to one second and random transposition into all twelve keys, so the model cannot memorize note-for-note correspondence and must learn the locally consistent style. The same representation supports cross-modal style transfer (a lead sheet paired with an unrelated style reference) and, after Stage-I alone, audio-to-MIDI retrieval whose accuracy is nearly unchanged when all candidates are transposed, showing the learned alignment is key-invariant and style-based.","pith_inferences":["Not tested in the paper: the same Q-Former style embedding could be probed dimension-by-dimension to see which coordinates encode swing, tempo, or dynamics; if such separation exists, style editing in latent space becomes possible without new training.","Not tested in the paper: because the representation is transposition-invariant and trained on short segments, it likely discards the exact micro-timing that distinguishes a tight swing feel; a tightly aligned variant with onset-time supervision would show whether that precision can be recovered.","Not tested in the paper: the local-consistency assumption implies the model will average over a style change inside a 4-bar window; a hierarchical or temporally adaptive style model would be needed for long-form compositions with phrase-level expressive arcs.","Not tested in the paper: the retrieval result suggests the style representation might serve as a general cross-modal music similarity measure, applicable to tasks like cover-song identification or style-based music search."],"forward_implications":["Given a lead sheet and an audio reference from the same song, the system generates a piano cover whose groove, dynamics, and tempo track the reference, as measured by Grooving Pattern Coherence, Velocity Contour Coherence, and Tempo Accuracy.","Given a lead sheet and an unrelated audio reference, the same model performs audio-to-symbolic style transfer, e.g., rendering the same melody as ragtime, bossa nova, or orchestral ballad depending on the chosen reference.","The Q-Former alone, without the generative stage, can retrieve the matching MIDI segment for an audio query from a pool of 128 candidates, and its accuracy barely changes when candidates are transposed across keys, indicating style-based rather than pitch-based alignment.","Only the 186M-parameter Q-Former and a 5M-parameter LoRA adapter are trained; the billion-scale audio and symbolic backbones stay frozen, making the approach comparatively cheap to adapt to new styles.","The performance difference between the full two-stage training and a no-pre-training ablation is largest on multi-instrumental audio, suggesting the contrastive, matching, and generative objectives matter most when style must be separated from richer audio textures."],"supporting_citations":[{"why":"Supplies the Q-Former architecture, self-attention masks, and hard-negative mining strategy that the whole bridging method builds on.","marker":"[16]"},{"why":"Supplies the frozen audio language model whose hidden states are treated as the source of style information.","marker":"[24]"},{"why":"Supplies the frozen symbolic music language model that generates the piano arrangement.","marker":"[3]"},{"why":"Supplies the in-distribution paired audio–MIDI dataset used for training and evaluating piano cover generation.","marker":"[36]"},{"why":"Supplies a cross-genre piano dataset used to encourage stylistically varied training pairs.","marker":"[37]"},{"why":"Serves as the auto-encoder-based audio-to-symbolic arrangement baseline that the complete system must beat.","marker":"[15]"},{"why":"Serves as the transformer-based piano cover generation baseline for objective and subjective comparison.","marker":"[32]"},{"why":"Provides the LoRA adapter that lets the frozen symbolic model accept the new lead-sheet and style conditioning.","marker":"[35]"},{"why":"Serves as the cross-modal alignment baseline for the audio-to-MIDI retrieval comparison.","marker":"[51]"},{"why":"Supplies the lead-sheet transcription system that turns audio into content input for all compared methods.","marker":"[14,42]"}],"fun_headline_variants":["Cross-modal bootstrapping: audio style to piano arrangements","Q-Former learns song feel from audio, then writes piano MIDI","Bridge audio and music models with a Q-Former for style transfer","Learn piano style from reference audio via cross-modal alignment"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that style is consistent within each paired 10-second audio clip and 4-bar MIDI segment, so random temporal shifts and twelve-key transposition still leave the model a stable style signal to learn; if style shifts inside that window, or if the loose alignment throws away the timing cues that define the groove, the training signal degrades.","fun_headline_variants_meta":{"raw":{"variants":["Cross-modal bootstrapping: audio style to piano arrangements","Q-Former learns song feel from audio, then writes piano MIDI","Bridge audio and music models with a Q-Former for style transfer","Learn piano style from reference audio via cross-modal alignment"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000169,"raw_usage":{"total_tokens":1250,"prompt_tokens":913,"completion_tokens":337,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":529,"completion_tokens_details":{"reasoning_tokens":266}},"tokens_in":529,"tokens_out":337,"duration_ms":4094,"temperature":1.0,"reasoning_tokens":266,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-08T00:58:27.909195+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Build a test set of 4-bar audio–MIDI pairs in which the style clearly changes at the midpoint (for example, straight eighths switching to swing, or a sudden tempo and texture break), generate covers conditioned on these clips, and count whether the second half's groove appears in the symbolic output. If the output reflects only an average of the two styles, the local-consistency assumption that carries the method gives way.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Supplies a cross-genre piano dataset used to encourage stylistically varied training pairs."},{"cited_title":"Do music llms learn sym- bolic concepts? a pilot study using probing and inter- vention,","cited_arxiv_id":null,"evidence_quote":"Supplies the Q-Former architecture, self-attention masks, and hard-negative mining strategy that the whole bridging method builds on."},{"cited_title":"Audio-to- symbolic arrangement via cross-modal music repre- sentation learning,","cited_arxiv_id":null,"evidence_quote":"Supplies the frozen audio language model whose hidden states are treated as the source of style information."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Supplies the frozen symbolic music language model that generates the piano arrangement."},{"cited_title":"Exploring transformer’s potential on automatic pi- ano transcription,","cited_arxiv_id":null,"evidence_quote":"Supplies the in-distribution paired audio–MIDI dataset used for training and evaluating piano cover generation."},{"cited_title":"Exploring the internal mechanisms of music llms: A study of root and quality via probing and intervention techniques,","cited_arxiv_id":null,"evidence_quote":"Serves as the auto-encoder-based audio-to-symbolic arrangement baseline that the complete system must beat."},{"cited_title":"Mustango: Toward con- trollable text-to-music generation,","cited_arxiv_id":null,"evidence_quote":"Serves as the transformer-based piano cover generation baseline for objective and subjective comparison."},{"cited_title":"Lora: Low-rank adaptation of large language models,","cited_arxiv_id":null,"evidence_quote":"Provides the LoRA adapter that lets the frozen symbolic model accept the new lead-sheet and style conditioning."}],"review_version":1}