Unifying Symbolic Music Arrangement: Track-Aware Reconstruction and Structured Tokenization
Authors: Longshen Ou
Jingwei Zhao
Ziyu Wang
Gus Xia
Qihao Liang
Torin Hopkins
Ye Wang
Venue: NeurIPS 2025 poster ()
Keywords: music arrangement
symbolic music
conditional music generation
symbolic music tokenization
Primary area: applications
TL;DR: A unified framework for multitrack music arrangement that enables a single pre-trained symbolic music model to handle diverse arrangement scenarios, and REMI-z, a compact and modeling friendly tokenization scheme for multitrack symbolic music.

Abstract: We present a unified framework for automatic multitrack music arrangement that enables a single pre-trained symbolic music model to handle diverse arrangement scenarios, including reinterpretation, simplification, and additive generation. At its core is a segment-level reconstruction objective operating on token-level disentangled content and style, allowing for flexible any-to-any instrumentation transformations at inference time. To support track-wise modeling, we introduce REMI-z, a structured tokenization scheme for multitrack symbolic music that enhances modeling efficiency and effectiveness for both arrangement tasks and unconditional generation. Our method outperforms task-specific state-of-the-art models on representative tasks in different arrangement scenarios---band arrangement, piano reduction, and drum arrangement, in both objective metrics and perceptual evaluations. Taken together, our framework demonstrates strong generality and suggests broader applicability in symbolic music-to-music transformation.

Unifying Symbolic Music Arrangement: Track-Aware Reconstruction and Structured Tokenization
Longshen Ou Jingwei Zhao' Ziyu Wang Gus Xia
Qihao Liang Torin Hopkins Ye Wang
FSound and Music Computing Lab, School of Computing, NUS ' CCuurant Institute of Mathematical Sciences, New York University e Music Lab, MBZUAI
Abstract
We present a unified framework for automatic multitrack music arrangement that enables a single pre-trained symbolic music model to handle diverse arrangement scenarios, including reinterpretation, simplification, and additive generation. At its core a segment-level reconstruction objective operating on token-level disentan- gled content and style, allowing for flexible any-to-any instrumentation transfor- mations at inference time. To support track-wise modeling, we introduce REMI-2, structured tokenization scheme for multitrack symbolic music that enhances modeling efficiency and effectiveness for both arrangement tasks and uncondi- tional generation. Our method outperforms task-specific Mfate-of-the-ae art models on representative tasks in different arrangement scenarios band arrangement, piano reduction, and drum arrangement, in both objective metrics and perceptual evaluations. Taken together, our framework demonstrates strong generality and suggests broader applicability in symbolic music-to-musie transformation)
Introduction
Music arrangement is the art of adapting compositions for performance contexts that differ from their original forms (4). It plays central role many music creation process, including professional production, live performance, music education, and digital content creation. Automating this process can expand music accessibility and accelerate music creation. Although arrangement forms
e.g..
rewriting for different instruments (reinterpretation) 191, simplifying for solo performance (reduction)
29 30), adding new tracks (additive generation) [20 19 1 share common structure:
generating new music tracks conditioned on existing ones under explicit content and instrument
constraints. However, prior work typically addresses each task independently [42 30 29
using specialized model architectures and training schemes. Such designs lack tross-task generality, increase implementation cost, and fail leverage the musical knowledge learned by large-scale pre-trained symbolic models that could potentially improve the arrangement quality.
Meanwhile, generative modeling of symbolic music, i.e., music in notation-based formats, has advanced rapidly with large-scale pre-trained models that capture rich musical styles and structures via autoregressive modeling [17 23
22 Inspired by natural language processing, these models scale to billions of parameters and are trained on vast corpora. Although their unconditional generation quality has improved substantially, their applications real-world conditional generation remains relatively limited. Prior work primarily targets coarse-level control, such as style [3] 17] structure 40), polyphony level [22] or sentiment [18, while fine- mne-grained conditioning on existing musical content precisely what arrangement tasks demand-remait inderexplored
37
32
Demos and code:
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Condition Sequence CINSTRUMENT
Turget Sequence
<CONTENT>
<HISTORY>
Taxk
1
Band Arrangement
Task Piano Reduction
Tush 3
Arrangement
Figure 1: Overview of the proposed unified framework for each arrangement task. The symbol + denotes concatenation of component sequences. Music segments are decomposed into three subsequences: instrumenis, content, and target-side history. These components form the condition sequence, with the relevant tracks from the original music the target sequence. The model is trained to reconstruct the music from these components.
This underexploration is likely not due to the absence of pre-trained models their representational power, but to the lack of training objectives that support ficxible adaptation to the diverse requirements of arrangement tasks. While sequence-to-sequee -o-quuence fine-tuning is conceptually natural solution, relies on parallel
coassett-oollettionns of the same piece arranged for different instrumentations extremely scarce. Even when adopting some level of internal parallel data [29 30). these datasets constrain output directions to fixed mappings (e.g., orchestra to piano). In contrast, real-world arrangement demands far greater flexibility: the ability to transform arbitrary input instrumentations into arbitrary targets-i.e., support any-to-any transformations.
(e.g., orchestra and piano)-whice
To address these limitations, we propose unified framework for symbolic music arrangement that enables a pre-trained symbolic generative model to be fine-tuned across diverse tasks through a single self- supervuper training pipeline (Figure I This facilitates the transfer of musical knowledge from generative pre-training to improve arrangement quality, while reducing the need for task-specific model design. To achieve this, we designed context-awes aware, segment-level reconstruction objective: the model reconstructs multitrack music from its disentangled components, including content (notes executed) and style (instrumentation) We further observe that strictly time-ordered tokenizations (e.g., REMI+ Bii introduce redundancy and fragment track content (detailed in $2.2), hindering instrument-level control and modeling. To address this, we propose structured tokenization scheme that relaxes global time ordering while promoting track-wise continuity, enabling more consistent encoding across musical contexts. Our main contributions are as follows:
We propose unified framework for symbolic music arrangement that supports flexible instrumentation transformation across multiple typical arrangement scenarios, all without requiring parallel data. Central our approach is a shared reconstruction objective defined over token-level disentangled note properties, which enables single generative model to adapt through lightweight fine-tuning.
To support effective learning under the proposed objective, we introduce an efficient and modeling-friendly tokenization scheme for multitrack music that produces shorter se- quences with lower complexity, facilitates instrument-level control and modeling that are important for arrangement performance. further reduces note-level perplexity uncondi- tional generation, indicating potential utility beyond arrangement tasks.
Instantiated with a small model and modest-scale pre-training, our system outperforms task-specific SOTA baselines on three representative arrangement tasks, each corresponding to a distinct scenario-band arrangement (reinterpretaatoo)) piano reduction (simplification) and drum arrangement (additive generation) in both objective and subjective evaluations.

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2 Related Work
2.1 Automatic Arrangement in Multitrack Symbolic Music
Symbolic music urrangement encompasses a variety of tasks such as chord progression generation [38]. orchestration from lead sheets [34), and instrumentation transfer [42]. In this work, we focus on adapting multitrack music to new instrumentations- representative setting that requires fine-grained control over musical content and instrumentation.
Early supervised learning approaches rely on parallel datasets (e.g., piano-to-orchestra [5], band-to- piano 29 30)), which are expensive to construct and inherently constrain the direction of arrangement. Classification-base methods ( approach the arrangement problem by learning to assign instrument labels to each note from a dataset with fixed instrumentation. However, such models are limited in expressiveness, as they cannot modify musical content-e.g... adding, removing, or altering notes to suit a target instrument combination. Moreover, their reliance on fixed instrument sets hinders generalization to unseen combinations.
Recent self-supervised methods 41 avoid these constraints by guiding generation with pre- defined or autoregressively modeled high-level descriptors (e.g.. pitch histogram, note density). While effective for maintaining stylistic coherence, they separate style modeling from content realization. As result, instrument playing styles either fixed or modeled independently of input variations. Since musical material often changes between sections, this decoupling can degrade fidelity-causing arrangements fail to reflect core aspects such melody texture, resulting in noticeable perceptual divergence. This suggests more fidelity-orienter approach: integrating style modeling into the generation process allow dynamic adaptation to input content.
Additionally. infilling- ballengg-based models such as Composer's Assistant 20
19
although capable of
handling additive generation scenarios such as drum arrangement, do not preserve the music essence of the original composition and are thus unsuited for reinterpretation and simplification.
2.2 Symbolic Music Tokenization
Transformer-basee symbolic music modeling requires converting musical data into token sequences, a nontrivial task due to music's inherent multi-stream structure, i.e., multiple instruments playing concurrently. ABC-based notations 23 convert staff-based music into text-like symbolic representa- tions, which are well suited for classical sheet music. While for comtemporary music, many existing tokenization schemes operate on MIDI files and adopt linearized, note-event-based encoding where each musical note is represented as a tuple of attribute tokens (c.g., onset, pitch, duration, velocity, instrument). Some use absolute timing [13 12 39 [0], while others use metric durations (26) 14 31).
Among them, the REMI representation 14, originally designed for single-track music, has been extended to REMI+ 31 for flexible tokenization of multitrack music, by associating each note with
an instrument token.
However, existing time-ordered tokenization schemes such REMI+ face structural limitations that hinder track-wise control and modeling. By flattening multitrack music into a strictly time-ordered sequence, REMI+ interleaves events from different instruments, resulting in content fragmentation. As illustrated in Figure 3al notes from the same instrument (e.g., 1-29, distorted electric guitar, in orange) are interrupted by those from concurrent instruments (c.g., 1-80. synth lead, in red). This leads to two major issues. First, the lack of structured syntax makes it difficult to delincate track boundaries, hindering enforcement of user-specified instrument constraints and often leads to spurious instruments in arrangement outputs. Second, identical track-wise content can be tokenized differently depending on concurrent instrument activity. This context sensitivity reduces the repetition of typical per-instrument patterns in the training data-a a key factor for learning accurate "instrument syntax" determined by physical constraints and idiomatic playing patterns of each instrument, important for arrangement quality. While some recent work explores vocabulary-level compression (e.g., Byte Pair Encoding ITT) to reduce sequence length-an an approach orthogonal and complementary to tokenization scheme design-in does not address the structural issues discussed above.
3

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<INSTRUMENTS, 1-09, 1-33, <CONTENT>> 0-0, p-52, a.8,... <HISTORY>, 1-80, p-20, d-24,
1-80, 0-0, p-52, d-3, 104, 8,8, p d: i-33, p-1,, d-12,
Condition Sequence
<SEP>
Target Sequence
Figure 2: An example of the tokenized sequence for the band arrangement task. Special tokens <EPP>, <INSTRUMENT>, <CONTENT>, and <HISTORY> are used separate different components. Tokens starting with o-, i-, p-, d- represents the onset, instrument ID, pitch, and duration of notes.
3
Method
Our goal is to fine-tune single pre-trained symbolic music generative model across diverse arrange- ment tasks via a unified pipeline. The core is a segment-level reconstruction objective ($3.I) over
token-level disentangled representations of style (instrumentation) and content (musical notes). To
support rrak-wiie control and modeling, we introduce structured tokenization scheme ($3.2) that reduces content fragmentation and enhances learning under this objective.
3.1 Reconstruction from Token-Level Disentangled Multi-Streams with Context Awareness
Symbolic music offers a unique opportunity for token-level disentanglement: each note is represented as list of semantically independent tokens, each describing a unique property of the note (e.g., onset, duration, pitch, instrument), allowing content (what is played) and instrumentation (by whom it is played) be explicitly separated. This level of structural redundancy rare in natural language. where sub-word tokens are atomic.
Building this observation, we formulate arrangement self-supervised, segment-level recon- struction task. The input music is decomposed into three token greams--intrumme content, and preceding context-and a pre-trained symbolic music generative model is fine-tuned to reconstruct the desired tracks of the multitrack music from these components.
Let (9) denote the /-th segment music piece. The fine-tuning objective is defined as:
(()) log Po (Tusk(y (1))), (1 Task (y (t-)
where ! represents the model parameters, I(-) and C(-) extract instrument and content conditions, Susk and Task are filters that select task-specific source and target tracks respectively (detailed 3.3), and (((11) provides target-side history. Equation 1 i implemented using standard next-token prediction objective the sequence [condition] <sep> [target] (Figure 2), with cross-entropy loss computed only on the target subsequence.
Instrument condition. The instrument condition specifies the desired instruments to be used in the segment. During training, includes all instrument tokens from the target sequence. Their order encodes pitch register relationships across tracks: instruments with higher average pitch are placed earlier. At inference, users can specify arbitrary desired instruments (instrument control) and define their relative pitch register ordering (voice control).
The content condition is derived from the original composition, encoded content sequence without instrument information. To obtain it, we remove all instrument tokens from the original multitrack sequence, notes by onset time, then by descending pitch, and merge duplicates. This yields a clean time-ordered note sequence conveying what played, independent of how or by whom.
Content condition.
History condition. The history condition provides musical context from the preceding segment, encouraging inter-segment coherence inference time. During training, it is provided via teacher- forcing with the complete tokenized previous segment; at inference, is autoregressively generated. This mechanism helps maintain coherent arrangement style across segments, especially important for long-form (e.g., levell arrangement scenarios.
During training, the instrument, content, and history conditions are derived from the same music being reconstructed. The model thus learns to reinterpret musical content with various instrument combinations under specific contexts, enabling diverse arrangement behaviors.

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6-0 6-0
p-38 1:29 4- d-12 2:24122 3:36 c-d o-30 129 p-52
1-80 --
p-76.d-11 1-26111--2220006 0-0 p- 0.12 p-76
0:18 9:00
p-74
1-14
o-18
d-36
d-11 0-36
p-78 d-11 0-36 1:29 p-38 6-42
p-52 d-7 b-1
0-22 p-52 d-7 1:33 o-0
p-36 d-23
b-1
(a) REM++ tokenization, demonstrated with REMI-Z vocabulary.
(b) A REMI-z bar sequence containing four track se- quences.
Figure 3: REMI+ and REMI-Z tokenization for the same bar. Contents of the same instruments are highlighted with the same color. See Appendix A complete vocabulary.
3.2 REMI-z: Tokenizing Multitrack Music with Track-Wiss Continuity
As discussed in Section 2.2 strictly time-ordered tokenization schemes suffer from content frag- mentation, a structural limitation that hinders arrangement performance. To mitigate this issue, we propose REMI-Z, tokenization scheme that heuristically prioritizes track-wise continuity over global time ordering. Specifically. REMI-Z processes MIDI files into a list of bar sequences, each containing multiple track sequences, where each track corresponds to a unique instrument. Within each track, note events are sorted by their onset position, then by descending pitch, and grouped under a single instrument token. Tracks within bar then ordered by their average pitch (high to low), forming bar-level token sequence that ends with a special end-of-bar token. The complete vocabulary and tokenization examples are shown in Appendix A
This zig-zag organization-reelecee
in the REMI-Z name-offers several modeling advantages: (1) instrument-wise content is locally contiguous (Figure 3b). reducing content fragmentation and enabling clear track boundaries; (2) sequence length is reduced by eliminating redundant instrument tokens; (3) temporal structure is preserved both within individual track sequences and between bars.
We adopt REMI-Z to tokenize all data for both pre-training and fine-tuning. Beyond improving arrangement performance, we also observe that REMI-Z produces sequences with lower information entropy and, when used for unconditional generative training, leads better note-level modeling (see $ $5.4). This suggests that its track continutyy design not only benefits arrangement tasks, but may also support general symbolic music modeling.
3.3 Task Instantiations
We evaluate our method on three representative music arrangement tasks that reflect typical arrange- ment scenarios, each assessing different capabilities of the model: band arrangement (reinterpretation). piano reduction (simplification), and drum arrangement (additive generation).
Band Arrangement. This task evaluates the model's 's ability to reinterpret an existing piece using arbitrary combinations pitched instruments. The model must learn the properties and idiomatic playing styles of various instruments to reallocate
generate notes appropriately. Both Susk and Trsk are identity mappings (i.e., Sna = Tax (y) = y). and drum tracks are removed from the randomly remove subset of tracks from the content condition C(Sunsk (y) during training, while ensuring the melody's 's content is preserved (detailed in Appendix B.2). Duration tokens are also removed from the content stream, allowing the model to infer track specific note durations, i.e., articulations, suitable for interpreting the content under desired instrumentations. Segment length is set to 1 bar. This setup resembles [42), but differs in its more challenging setting: track-wise style priors available to guide the generation process.
input. To encourage creative rewriting,
Piano Reduction. This task simplifies ensemble music into solo piano accompaniment, aiming to preserve key harmonic and textural elements while ensuring pianistic playability. The Tink selects piano tracks, while Susk is the identity mapping. To ensure the reduction is meaningful, we filter training data to keep only segments where the piano part is sufficiently prominent (covering >40% of the pitch range). Drum tracks are removed from input, and the segment length is set to bar. The setup is similar to 30), but uses original MIDI piano tracks as targets instead of human-composed reductions, and does not include difficulty-lewe conditioning.
Drum Arrangement. The goal of this task is to generate a drum track for music that lacks one. Here, Siss extracts all pitched-instrument tracks, while Taak extracts the drum track. We use 4-bar segments since drum patterns often span across multiple bars. The model must recognize the underlying groove and phrase boundaries of the source music, enhance them with coherent and stylistically appropriate rhythmic patterns, and ensure proper transitions across grgments-requiring a stronger understanding
5

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of rhythmic ideas and structural organization. This track infilling setup is similar to [9], but the model is not allowed condition on future segments.
4 Experiments
4.1
Implementation Details
Our model, an 80M-parameter decoder-only Transformer, has hidden dimension of 768, 12 layers. 16-head attention, and context length of 2048 tokens (around 8x the longest bar in our dataset). The model first undergoes a standard next-token-prediction pre-training, and then was fine-tuned with the proposed objective. Pre-training used four RTX A5000 GPUs (batch size 12, 1 epoch), while fine-tuning used single A40 GPU (variable batch size, epochs). Pre-training adopted the Los Angeles MIDI dataset [15] (405K MIDI files, 4.3B tokens after REMI-z tokenization, 2% validation split) and fine-tuning was done with Slakh2100 [20 (1,289 training, 270 validation, 151 test MIDI files), featuring 34 pitched instruments and drums, with 24 tracks per piece. Detailed hyperparameter settings are in Appendix B.5 B.5
4.2 Baseline Models
For each task, compare our model against state-of-the-art (SOTA) task-specific baseline. For band arrangement, we adopt Transformer- VAE from [42]. the strongest previously reported model for multitrack arrangement without assumptions on track type or number. It combines Transformer- based long-term and inter-track modeling with a VQ-VAE generation module. For piano reduction, we compare with 30 (UNet), the most recent work this area. For drum arrangement, we adopt Composer's 's Assistant 2 (CA v2) [1), SOTA track infilling model capable of handling multitrack inputs and generating drum outputs. Existing drum--ppeciie models (e.g., (2]. [o] are unsuitable for our setting, as they assume a single melody or instrumental track input rather than general multitrack conditioning. Baseline's 's implementation details are in Appendix [.44
To demonstrate the impact of generative pre-training, an ablation variant of our model without pre-training (w/o PT) is used as baseline. Additionally, we include simple rule-based baselines as non-learning references, designed to provide naive solutions with minimal musical knowledge, helping contextualize the difficulty of arrangement tasks. For band arrangement, Rule-Based distributes notes evenly by pitch across instruments, serving as a naive reinterpretation strategy. For piano reduction, we use Rule-F (a flattened multitrack where the piano plays all notes), which reflects an overcomplete reduction prioritizing coverage, and Rule-O (the original piano track), which provides playability-gurrateed guaranteed but musically incomplete reduction. For drum arrangement, the original drum track (Ground Truth) is included anonymously in the human evaluation as upper bound on perceptual scores.
4.3 Objective Evaluation
Objective metrics measure similarity between model outputs and target sequences, assuming closer resemblance to numan-created music indicates higher naturalness and musicality. Following [19, 30), use note-level Fl to measure similarity between model outputs and target sequences. Specifically, we compute Note F1 (correct onset and pitch) and Note; F1 (additional correct instrument prediction), both under 16th-note quantization for fair comparison with baselines.
For piano reduction and drum arrangement, the same models are used in objective and subjective evaluations. For band arrangement, models are separately trained without random track deletion to ensure deterministic outputs. Baseline models are also modified by excluding its prior model to remove long-term context hints, ensuring evaluation fairness.
In addition to the modifications described above, we introduce three task-specific metrics for band arrangement. First, Instrument Intersection over Union (I-IoU) evaluates the accuracy of instrument control. Second, Voice Error Rate (VER) measures generated output and the reference, reflecting how well the model follows the voice conditions specified by instrument token ordering. Third, Melody FI (Mel F1) computes the Note Fl score on melody tracks, estimated as the tracks with the highest average pitch in the output and reference, to
similarity in voice features between the

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Table 1: Objective evaluation results for the band arrangement task.
Statistical significance is
indicated as follows: * for < 0.05, f for < 0.01, and for < 0.001.
Model
I-IOU f
VER 4
Note 1
Note; Fl
Mel
Transformer VAE
97.5
35.0
49.5
40.0
24.7
Transformer w/ REMI+
95.0
18.2
94.4
76.0
68.8
Transformer w/ REMI-Z
$99.5
49.9
$97.8
$77.5
$77.8
+ Pre-training (Ours)
99.8
7.6
97.5
87.0
84.5
voice history
99.6
17.6
97.2
84.3
81.5
100.0
9.0
97.6
77.4
79.4
assess how well the original melody is preserved-an important factor for perceived fidelity, Detailed definitions and computation procedures are provided in Appendix C.1
Among all tasks, band arrangement is strong testbed for evaluating controllability and generaliz- ability because requires the highest flexibility without assumptions on target instrument types or counts. Hence it serves two additional purposes: (1) validate key design choices in fine-tuning objective, particularly the use of voice-aware instrumentation and segment-level history conditioning: and (2) prove the effectiveness of the proposed tokenization schemes in arrangement task. When comparing tokenization schemes, statistical significance is computed by Wilcoxon signed rank test 33 For broader analysis of tokenization's 's impact on unconditional generation, see $ 55,4
4.4 Human Evaluation
To complement similarity- based objective metries and assess perceptual quality and creativity, we conducted human evaluations. Full-piece arrangements were generated by all models and evaluated on 5- 5-point scale (1: very low, very high). For band arrangement, models were tested across three instrument combinations with different complexity: string trio (3 tracks), rock band (4 tracks), and jazz band (7 tracks). Three metrics were used across band, piano, and drum arrangement tasks: Coherence, which evaluates the natural flow of the arrangement and the consistency of each instrument's playing style throughout the piece; Creativity, which assesses the degree of innovation in the arrangement under the constraints of the music's content and style; and Musicality, which measures the overall musical appeal and aesthetic quality of the arrangement. Further details on the metrics, ensemble settings, questionnaire, and evaluation process are in Appendix D
Task-specifi metrics were introduced for the distinct evaluation needs of each arrangement scenario. For band arrangement, Faithfulness measures resemblance to the original in melody and overall feel, while Instrumentation assesses the appropriateness of each instrument's role within the ensemble and their harmony. For piano reduction, Faithfulness is also adopted but without melody preservation requirements, and Playability assesses the feasibility of the generated contents played by human pianists. For drum arrangement, Compatibility measures how well the drum track blends with other instruments, and Phrase Transition assesses the smoothness of transitions between musical phrases. We report mean and standard error of mean in result tables. Significance tests were conducted between our model and the SOTA baselines using within-subject (repeated-measunn measures) ANOVA (28).
5
Results
5.1
Band Arrangement
5.1.1
Objective Evaluation
Tokenization impact. Table 1 shows that model adopted REMI-Z significantly outperforms that adopted REMI+ across all objective metries. enables stronger instrument control (I-IOU: 99.5% vs. 95.0%) and voice control (VER: 9.9% vs. 18.2%). aligning outputs better with user-specified conditions. The improvement Note Fl (+6.7%) confirms higher reconstruction quality, while the gain in Note; F1 (+1.9%) highlights enhanced instrument- wiss modeling. Furthermore, the substantial increase Mel (+9.0%) suggests REMI-Z enable the model better identify the melody components from content sequence, which is improtant for arrangement fidelity.
7

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Table 2: Band arrangement subjective evaluation results. Fa., Co., In., Cr., and Mu. represent Faithfulness, Coherence, Instrumentation. Creativity, and Musicality, respectively.
Model
Fa. t
Co. f
In. f
Cr.
Mu.
Rule-Baser 3.46:10.14
3.05
2.8940.14
3,00+0.12
3.07+0.13
Transformer VAE
2.65=+0.10
2.70=0.11
2.72+0.12
3.00+0.13
2.72+0.11
Ours
$3.77+0.13
+3.47=0.15
13,49
13.40+0.13
13.47=0.14
w/o PT
3.19420.13
2.82=0.13
2.86+0.14
2.93+0.12
2.75+0.13
Table 3: Piano reduction results. The Pl. represents Playability score.
Model
Fl
Fa. f
Co.
Pl.
Cr. f
Mu. T
Rule-F
3.93
3.59
3.14
2.96+0.13
3.34+0.14
Rule-O
2.75+0.13
3.49
4.07
2.6240.14
2.96#0.14
UNet
58.3
2.97
2.90:0.15
3,4740.13
2.82+0013
2.78=20.13
Ours
85.5
13.6340.13
13.64
"3.86+0.13
"3.14+0.12
13,48
w/o PT
78.4
2.25+0.13
2.58+0.16
3,2940.15
2.6740.15
2.26+00.14
Pre-training benefit. Pre-training brings clear gains by transferring musical knowledge useful for arrangement. It improves three key aspects: (1) lower VER 2.)3%) indicating more effective voice control; (2) higher Note, F1 (+9.5%), reflecting enhanced instrument-wir content modeling; and (3)
the highest Mel (84.5%), indicating stronger melody preservation. Probing analysis (Appendix E further shows that pre-training strengthens the alignment between token embeddings and musical concepts useful for arrangement, such instrument roles and chord progression.
Comparison with Transformer- VAE. Our model outperforms Transformer- VAE by a wide margin. In particular, achieves much higher Note F1 (97.5% vs. 49.5%), Note; Fl (87.0% 40.0%), and Mel FI (84.5% vs. 24.7%), highlighting the advantage of our context-aware, content- and mnstrument-conditione generation approach over the latent inference used in Transformer- VAE.
Ablation: voice and history conditioning, Removing voice information from instrument conditions degrades voice control (VER: +10.0% demonstrating the effectiveness of our voice control method. also lowers per-instrument F1 (Note;: -2.7%) and Mel F1 3.0%), indicating that voice-order information serves useful hint for inferring instrument roles during arrangement. Excluding history conditioning results in even larger drops in Note, F1 (-9.6%) and Mel (-5.1%) validating that temporal context facilitates accurate reconstruction, laying the foundation for coherent song-level arrangement.
5.1.2 Human Evaluation
Strong subjective performance. As shown in Table Our model achieves the highest scores all subjective metrics than all baselines, demonstrating its ability to generate coherent, stylistically appropriate, and musically appealing arrangements while preserving core musical essence.
Transformer- VAE underperforms. It lags significantly behind our model in every subjective criterion. Notably, its lower Faithfulness score (-1.12) reflects difficulty in preserving core musical content, while low ratings in Instrumentation (-0.77) and Coherence (-0.77) suggest weaker track-wise modeling and non-idiomatic instrument usage, as well as insufficient coherence between segments. Overall Musicality (-0.75) also falls below our model. It even scores lower than the rule-based baseline, though the latter suffers from inherent limitations in musicality and coherence.
Pre-training improves quality. Removing pre-training reduces scores across all metries, with notable drops in Faithfulness (-0.58) and Musicality (-0.72), reinforcing the importance of musical knowledge transfer from generative pre-training for content retention and overall perceptual quality.
5.2 Piano Reduction
Best overall quality. As shown in Table 3 our method obtains the highest ratings in F1 (85.5%), Coherence (3.64), Playability (3.86), and Musicality (3.48), while also maintaining strong scores in Faithfulness (3.63) and Creativity (3.14). It significantly outperforms UNet across all subjective

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Table Drum arrangement results. Comp. and Tr. represent Compatibility and Phrase Transition score respectively.
Model
Comp.
Co.
Tr.
Cr.
Mu.
Ground Truth
100.0
4.31
4.18=0.10
3.36+0.13
3.16=0.12
3.78=0.12
CA v2
20.3
3.82+0.13
4.05=0.12
2.86+0012
2.58=0.11
3.19
Ours
79.3
3.91+0.12
4.03=0.10
f3.77+0.12
$3.27=0.44
13.57=0.13
w/o PT
2.4940.16
2.19=0.12
2.21+0.14
2.82=0.15
2.05
Table 5: Tokenization scheme comparison on unconditional generation.
Tokenizer
Tou 4
Tass
Har 4
PPL
PPLnjenn
REMI+ 225.91
4.03
41.68
116.20
3.00
REMI-Z (Ours)
151.68
2.77
29.43
84.11
4.50
metrics, with especially large margins in Faithfulness (+0.66), Coherence (+0.74), and Musicality (in.7))-indicating better content preservation, cross-segment continuity, and overall musical quality. Pre-training again proves essential: without it (w/o PT), performance drops notably across all metrics.
Balanced fidelity and playability. Compared to rule-based methods, our model achieves better trade-off between fidelity and playability. Rule-F preserves original content well (Faithfulness: 3.93) but results in poor playability (3.14), while Rule-O achieves the highest playability (4.07) but at the expense of faithfulness (2.75). In contrast, our method produces reductions with balanced Faithfulness (3.63) and Playability (3.86).
5.3 Drum Arrangement
Best subjective quality among learned models. As shown in Table our model outperforms CA v2 in all subjective metrics except Coherence (4.03 vs. 4.05), and significantly improves Creativity (3.47 vs. 2.58) and Phrase Transition (3.27 vs. 2.86), which are essential for engaging, structurally-aware drum arrangements. It also achieves the highest Musicality score (3.57), closely approaching ground truth (3.78). Again, the w/o PT variant performs poorly across all metrics.
Improved phrasing and variation. Compared to CA v2, which often repeats similar drum patterns throughout piece, our model produces more varied and context-aware rhythms that better reflect musical phrasing. The higher Phrase Transition score (+0.91) supports this observation, demonstrating the model's ability to capture structural changes in music during generation.
5.4 Tokenization Efficiency and General Modeling Advantages
Additionally, we evaluate REMI-Z against REMI+ assess its efficiency and effectiveness for generative modeling of symbolic multitrack music. We report several metrics to evaluate compactness of tokenization schemes and their unconditional modeling performance: 1) average tokens per bar (Tbar), 2) average tokens per note (Tnoe). 3) Shannon entropy of bar-level token sequences (Haar), and 4) note-level perplexity (PPL.ou)). Note that PPL the aggregated conditional probability of all note attribute tokens, normalized by the number of notes, enabling fairer comparison across tokenization schemes. Calculations are detailed Appendix C.22
Compactness. Tokenizing Slakh2100 with REMI-Z yields a 32.9% reduction in sequence length per bar (151.68 vs 225.91) and fewer tokens per note (2.77 vs 4.03), effectively reducing training and inference computational costs.
Representational Simplicity. REMI-Z also produces lower bar-level Shannon entropy (29.43 41. 6.68 bits/token) on Slakh2100 compared to REMI+, despite representing the same underlying musical content. This indicates reduced information redundancy, and suggests that REMI-z sequences consist of more predictable tokens, potentially facilitating the learning of generative models.
Note-Lever Modeling Advantage. We train unconditional generation models on S1akh2100 with both tokenizations and compare note-level perplexity with the same architecture as our arrangement models. The model adopting REMI-z achieves substantially lower note-level perplexity (84. vs.

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116.20), indicating better modeling of complete musical notes. Since notes form the atomic units of music, improvements in note-level modeling directly contribute lower uncertainty modeling bars and full compositions, suggesting utility in general symbolic music modeling beyond arrangement tasks. While REMI+ slightly outperforms in token-level perplexity, this does not translate to better modeling of higher-level structures.
Conclusion
We presented unified framework for automatic music arrangement, centered around a reconstruction objective that enables diverse arrangement tasks without requiring parallel data, enabling knowledge transfer from generative pre-trained symbolic music models for arrangement tasks. Our approach also integrates a structured tokenization scheme, REMI-z, which convert multitrack music to compact and easy-to-moder token sequences. Experimental results on band arrangement, piano reduction. and drum arrangement show that our method consistently outperforms task-specific baselines in both objective and subjective evaluations. These results suggest the potential of our framework as general solution for symbolic music-to- mu-ic transformation.
Acknowledgements
The authors would like thank Chenfei Kang for providing valuable resources that supported the validation of the proposed idea, and Yisong Miao for insightful comments on training and coherence of language models, which substantially benefited this work.
References
[1] Guillaume Alain and Yoshua Bengio. Understanding intermediate layers using linear classifier probes. arXiv preprint arXiv: 161.v:1610.01644, 2016.
[2] Giorgio Barnabò, Giovanni Trappolini, Lorenzo Lastilla, Cesare Campagnano, Angela Fan, Fabio Petroni, and Fabrizio Silvestri. Cycledrums: automatic drum arrangement for bass lines using cyclegan. Discover Artificial Intelligence, 3(1):4, 2023.
[3] Kristy Choi, Curtis Hawthorne, Ian Simon, Monica Dinculescu, and Jesse Engel. Encoding musical style with transformer autoencoders. In International conference on machine learning. pages 1908. PMLR, 2020.
[4] Vince Corozine. Arranging music for the real world: classical and commercial aspects. Mel Bay Publications, 2015.
[5] Léopold Crestel and Philippe Fsling. Live orchestral piano, system for real-time orchestral music generation. arXiv preprint arXiv: 1609.01203, 2016.
[6] Rishabh Dahale. Automatic Drum Accompaniment Generation from Melody. PhD thesis, Indian Institute of Technology Bombay, 2022.
[7] Roger B Dannenberg and Sukrit Mohan. Characterizing tempo change in musical performances. In ICMC, 201
[8] Hao-Wen Dong, Ke Chen, Shlomo Dubnov, Julian McAuley, and Taylor Kerg-Kirkpatrick. Mul- titrack music transformer. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1 1-5. IEEE, 2023.
[9] Hao- Wen Dong, Chris Donahue, Taylor KirgKKirkaatrick, and Julian McAuley. Towards automatic instrumentation by learning to separate parts in symbolic multitrack music. In International Society for Music Information Retrieval Conference, 2021.
[10] Jeffrey Ens and Philippe Pasquier. Mmm Exploring conditional multi-track music generation with the transforerr. ArXiv, abs/2008.06048. 2020.
10

---

[11] Nathan Fradet, Nicolas Gutowski, Fabien Chhel, and Tean-Pierre Briot. Byte pair encoding for symbolic music. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2001-2020, Singapore, December 2023. Association for Computational Linguisties.
[12] Josh Gardner, Ian Simon, Ethan Manilow, Curtis Hawthorne, and Jesse Engel. M13: Multi-task multitrack music transcription. ArXiv, abs/211 1 .03017, 2021.
[13] Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Noam M. Shazeer, lan Simon, Curtis Hawthorne, Andrew M. Dai, Matthew D. Hoffman, Monica Dinculescu, and Douglas Eck. Music transformer: Generating music with long-term structure. In International Conference on Learning Representations, 2018.
[14] Yu-Siang Huang and Yi-Hsuam Yang. Pop music transformer: Beat-based modeling and generation of expressive pop piano compositions. In Proceedings of the 28th ACM international conference maltimedia, pages 1180-1188, 2020.
[15] Aleksandr Lev. Los angeles midi dataset: Sota kilo-scale midi dataset for mir and music ai purposes. In GitHub, 2024.
[16] Mike Lewis. Yinhan Liu, Naman Goyal, Marjan Ghazvininejad. AbdeIrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint 1910 2019.
[17] Peiling Lu, Xin Xu, Chenfei Kang, Botao Yu, Chengyi Xing, Xu Tan, and Jiang Bian. Musecoco: Generating symbolic music from text. arXiv preprint arXiv:2306. 000 10, 2023.
[18] Dimos Makris. Kat R Agres, and Dorien Herremans. Generating lead sheets with affect: A novel conditional seq2seq framework. In 2021 International Joint Conference on Neural Networks (JJCNN) pages 1-8. IEEE, 2021.
[19] Martin Malandro. Composer's Assistant 2: Interactive Multi-Track MIDI Infilling with Fine- Grained User Control. In Proc. 25th Int. Society for Music Information Retrieval Conf., San Francisco, CA, USA, 2024.
[20] Martin E Malandro. Composer's assistant: An interactive transformer for multi-track midi infilling. arXiv preprint arXiv:2301.12522) 2023.
[21] Ethan Manilow, Gordon Wichern, Prem Seetharaman, and Jonathan Le Roux. Cutting music source separation some slakh: A dataset to study the impact of training data quality and quantity. In 2019 IEEE Workshop on Applications of Signal Processing Audio and Acoustics ( (WASPAA), pages 45-49. IEEE, 2019.
[22] Philippe Pasquier, Jeff Ens, Nathan Fradet, Paul Triana, Davide Rizzotti, Jean- -aattstt Rolland, and Maryam Saf. Midi-gpt: A controllable generative model for computer-assiste assister- multitrack music composition. arXiv preprint arXiv:2501.17011, 2025.
[23] Xingwei Qu, Yuelin Bai, Yinghao Ma, Ziya Zhou, Ka Man Lo, Jiaheng Liu, Ruibin Yuan, Lejun Min, Xueling Liu, Tianyu Zhang, et al. Mupt: A generative symbolic music pretrained transformer. arXiv preprint arXiv:22044.066 06393,, 2024.
[24] Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. Improving language understanding by generative pre- pre-training. 2018.
[25] Colin Raffel. Learning-Bass -Based Methods for Comparing Sequences, with Applications to Audio- Io-MIDI Alignment and Matching. Phd thesis, Columbia University, USA, 2016.
[26] Yi Ren, Jinzheng He, Xu Tan, Tao Qin, Zhou Zhao, and Tie- Yan Liu. Popmag: Pop music accompaniment generation. In Proceedings of the 28th ACM international conference on multimedia, pages 1198 1178-1206 1206, 2020.
[27] Anna Rogers, Olga Kovaleva, and Anna Rumshisky. A primer in bertology: What we know about how bert works. Transactions of the Association for Computational Linguistics, 8:842- 866, 2020.
11

---

[28] Henry Scheffe. The analysis of variance, volume 72, John Wiley & Sons, 1999.
[29] Moyu Terao, Yuki Hiramatsu, Ryoto Ishizuka, Yiming Wu, and Kazuyoshi Yoshii. Difficulty- aware neural band-to-piano score arrangement based on note-and statistic-level criteria. In ICASSP 2022-2022 IEEE International Conference Acoustics, Speech and Signal Processing (ICASSP), pages 196-200. IEEE, 2022.
[30] Moyu Terao, Eita Nakamura, and Kazuyoshi Yoshii. Neural band-to- -i--nooo-ooooooooo score arrangement with stepless difficulty control. In ICASSP 2023-2023 IEEE International Conference Acoustics, Speech and Signal Processing (ICASSP), pages 1-5, IEEE, 2023.
[31] Dimitri von Rütte, Luca Biggio, Yannic Kilcher, and Thomas Hofmann. Figaro: Controllable music generation using learned and expert features. In The Eleventh International Conference on Learning Representations, 2023.
[32] Yashan Wang, Shangda Wu, Jianhuai Hu, Xingjian Du, Yueqi Peng, Yongxin Huang, Shuai Fan, Xiaobing Li, Feng Yu, and Maosong Sun. Notagen: Advancing musicality in symbolic music generation with large language model training paradigms. arXiv preprint arXiv:2502. 18008. 2025.
[33] Ziyu Wang. Ke Chen, Junyan Jiang, Yiyi Zhang, Maoran Xu. Shuqi Dai, Xianbin Gu, and Gus Xia. Pop909: A pop-song dataset for music arrangement generation. arXiv preprint arXiv:2008.077 042, 2020.
[34] Ziyu Wang, Lejun Min, and Gus Xia. Whole-song hierarchical generation of symbolic music using cascaded diffusion models. arXiv preprint arXiv:2405.09900 2024.
[35] Ziyu Wang, Dejing Xu, Gus Xia, and Ying Shan. Audio-to-symbolie arrangement via cross- modal music representation learning. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 181-185. IEEE, 2022.
[36] Frank. Wilcoxon. Individual comparisons by ranking methods. Biometrics, 1:196-202, 1945.
[37] Shangda Wu, Yashan Wang, Xiaobing Li, Feng Yu, and Maosong Sun. Melodyt5: A unified score-to-score transformer for symbolic music processing. arXiv preprint arXiv:2407.02277, 2024.
[38] Li Yi, Haochen Hu, Jingwei Zhao, and Gus Xia. Accomontage2: A complete harmonization and accompaniment arrangement system. arXiv preprint arXiv:2209.0033: 2022.
[39] Mingliang Zeng, Xu Tan, Rui Wang, Zeqian Ju, Tao Qin, and Tie- Yan Liu. Musicbert: Symbolic music understanding with large-scale pre-training. ArXiv, abs/21 106.0000000 05630, 2021.
[40] Xueyao Zhang, Jinchao Zhang, Yao Qiu, Li Wang, and Jie Zhou. Structure-enhanced pop music generation via harmony- aware learning. In Proceedings of the 30th ACM International Conference Multimedia, pages 1204-1211 2022.
[41] Jingwei Zhao, Gus Xia, and Ye Wang. Q&a: query- based representation learning for multi- track symbolic music re-arrangement. In Proceedings of the Thirly-Second International Joint Conference on Artificial Intelligence, pages 5878-5886, 2023.
[42] Jingwei Zhao, Gus Xia, Ziyu Wang, and Ye Wang. Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024.
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A REMI-Z Tokenization
A.1 Vocabulary
Table 6 presents the complete vocabulary of our proposed REMI-Z tokenizer, detailing the value ranges for each token type. For instrument tokens, the values correspond MIDI program numbers (0-127), with 128 specifically designated for the drum set. For pitched instruments, pitch token values directly map MIDI pitch numbers, while drum set pitches encoded as MIDI pitch + 128. Both position and duration tokens are quantized in units a 48th note (one-third a sixteenth note). Time signature and tempo tokens undergo specific quantization before token mapping; for detailed quantization rules, please refer to our code implementation. All tokens listed in the table were utilized during pre-training. including time signature and tempo tokens serving bar-level properties, with example REMI-Z sequences illustrated in Figure 4b
During fine- tuning, we simplified the token set by excluding time signature and tempo tokens. This decision was supported by our analysis of the fine-tuning dataset (Slakh2100 100 (2T]), where 94.8% of songs use 4/4 time signatures. Consequently, we restricted our fine-tuning to 4/4 songs and omitted time signature tokens, following practices in related works 35 We also excluded tempo tokens since tempo adjustments in digital audio workstations are typically handled global parameter, affecting only tempo tokens without altering the any other tokens. This simplification assumes that compositional and performance styles remain consistent across different tempos. However, this assumption may not hold for datasets with significant tempo variations. Therefore, for future research requiring time-signature- tempo-specific characteristics, we recommend including these tokens during
Velocity tokens were excluded from both pre-training and fine-tuning phases. This decision reflects our focus on compositional quality rather than performance naturalism, aligning with previous approaches in band and piano arrangement studies 42 30]. However, if velocity information is deemed crucial for generation, our model architecture readily accommodates the addition of velocity tokens each note without introducing content fragmentation issues, if the REMI-Z note organization order is maintained.
A.2 Example
We name our tokenization scheme REMI-z for its distinctive "zig-zag" encoding pattern for musical notes within each bar, as illustrated Figure 4al In this scheme, notes are encoded hierarchically: first grouped by tracks (instruments), then organized bar by bar. This track-first approach ensures notes from the same instrument remain clustered together, thereby enhancing the model's ability to learn instrument-specing patterns. The resulting REMI-z sequence is demonstrated in Figure 44b In contrast, REMI+ [3]] employs column- -iise encoding strategy, strictly ordering notes by their temporal positions. While this approach effectively captures global temporal relationships, it disperses notes from the same instrument throughout the sequence, potentially complicating instrument-specife pattern learning. We leverage such temporal ordering in our content sequence (excluding instrument tokens), as shown in Figure
Table 6: REMI-Z vocabulary with pitch token distinctions
Meaning
Token
X's range
Instrument type
1-X
0~-188
Note's within-bar position
o-X
0~127
Note's pitch (non-drum)
p-X
0~127
Note's pitch (drum)
P-X
128-~255
Note's duration
d-X
0~~177
End of bar
b-1
Time signature
S-X
0~2533
Tempo
L-X
0~44
13

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40
Synth Lead Chi
Clean Electric Guttar Ch1
S3 Semionns)
Electric Bass Chn
(a) 4-bar musical segment with orange arrows illustrating the "zig-zag" encoding order of notes REMI-z tokenization.
(s-9 (optional)
1-80 o-18 p-74 d-14 o:36 p-76 d-11 1-26 o-0 p-60 1-29 o-0 p-36 d- 1-12
p-36 d-12 0-18 p-48
0-24 p-38 d-8 o-30 p-52 6-11
o-38
p-36 d-10
0-0
d-23 b-1
(e-9 (optional)
1-80 o-0 p-0 6-12
p-76 p-76
o-18 p-74 4-
3-36
p-7 0-42 p-72 d-10 1:26 o-0 p-67
2nd
p-32 d-7 o:30 p-44 d-10
3-36
p-32
d-10
4:42
0:
d-9
p-44 b-1 o:38
(a-9 1-35) (optional)
1-80 o-38 p-89 6-42
p-72
1-28
p-64
d-11
d-17
1-29
d-8
d-10 o-12
3rd bar
p-33 d-8 6-18 p-18 6:19 p-57
d-19
p-52
d-19
p-48
d-8
2-24
p-33
d-8
o-30
p-48
o-36
p-33 d-4 0-42
1:33 0:0 d o
(e-9 t-35) (optional)
o-18 p-74
p-72
1:26 6-6 p-64 d:11 0-18 p-62 d-12
o-30
60
38
d-5
d
1-29
p- d-8 p-52
d-9 c-12 p-40 o-18 p-62
d-12
p-59
d:12
5-55
p-52
d-11 0-24
p-
p-60 p-52
o-36 p-62 p-40 o-42 d-13 p-57 d-13 d-13
d-8 i:33 d-8 o-12 o-24
p-40 6-8 o-36 p-40 d-8
(b) REMI-Z sequence tokenized from the musical segment above. Track sequences are color-coded by instrument: synth lead (red), clean electric guitar (hlue), distorted electric guitar (orange), and electric bass (purple).
Figure An example of REMI tokenization.
a-0 p-80 d-26 p-36 d-23 a-12 p-38 d-12 o-18 p-74 d-14 p-48 d-12 o-24 p-36 d-8 o-30 p-52 d-11 o-36 p-76 p-36 6-10 4-42 p-52 d-7 b-1
1st bar
o-0 p-74 d-14 d-10
p-67 d-13 p-43 6-17 e-6 p-43 d-7 o-12 p-76 d-5 p-31 d-6 o-18 p-74 d-10
6-12 p-47 p-32
p-67
2nd bar
p-44 p-32
p-72
p-87
p-44
p-71
p-44
6-10
p-72 6:10 p-64 d-11 p-44 d-9 b-1
o-0 p-45 p-33 o-6 p-64 d-11 p-45 d-10 o-12 p-45 p-33 d-8 o-18 p-60 d-19 p-57 d-19 p-52 d-19
3rd bar
p-48 6-8 e-24 p-45 p-33 d-8 o-30
d-8 o-36 p-69
d-8 p-33 4-42
p-72 d-5 p-48 b-1
o-0 p-76 d-5 p-40
o-8 p-74 6-10 p-54
p-52 6-9 o-12 p-10 d-8 o-18 p-74 p-62 d-12 p-59 d-12
4th bar
p-55 d-12 p-52 d-11 o-24 p-72 d-5 4-40 o-30 p-72 d-4 p-80
d-11 o-36 d-11
d-8 o-2 p-72 d-13 p-64 d-11 p.60 d-13 p-57 d-13 p-53 d-13 p-48 b-1
Figure 5: The content sequence obtained by applying the operator C(-) to the REMI-Z sequence shown in Figure 4b
B
Implementation Details
B.1 Key Normalization
Transpoiition-qquivaiiane is a crucial property in symbolic music: a composition's musicality remains unchanged under global pitch shifts (uniform pitch adjustment across all notes). However, uneven key distribution in datasets can lead to data sparsity during training, potentially causing models to perform inconsistently across different keys. Two approaches address this issue: (1) data augmentation through systematic semitone transpositions 0, +1, +2 ... 19 42 or (2) normalizing all songs to a common key (e.g., S. C major and A minor) [17] We adopt the latter approach, imple- menting a modified version of [17]ss 's method. Our implementation uses key dictionary mapping from each of the 24 keys (12 major and 12 minor) to a 12-dimensional binary vectors, where Is indicate scale notes. Key detection is performed by computing dot products between these vectors and song's pitch histogram, with the highest-scoring key determining the transposition needed to normalize to C major or A minor. Further details can be found in the code.
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B.2 Random Track Deletion
To encourage creativity in band arrangement and prevent the model from wer-relying on direct copying notes from the input, we introduce a random track deletion mechanism during training. The intuition is that a well-trained model should be able infer suitable instrumental content even if such content does not exist in the original music composition.
Concretely, given a content sequence with multiple instrument tracks, we randomly delete all notes that belongs to a subset of instruments before calculating the content sequences and feeding to the model. The number of instruments
delete is sampled from Poisson distribution with À = max / 1), where Z is the set of non-melodic instruments in the content sequence. The melodic track is estimated by the track with highest average pitch, and does not involves in the track deletion to ensure the melody's 's content is preserved. Then, the deletion count is clipped to ensure at least one instrument remains. corresponding number of instruments are uniformly sampled without replacement and all tokens associated with these instruments are removed from the input. Then the content sequence calculated on this modified music sequence.
Importantly, the target sequence remains unchanged: it includes all instruments and notes removed from the input. This setup requires the model reconstruct missing tracks based solely the remaining musical context. In doing so, the model learns not only to replicate observed content but also infer plausible notes for desired instruments in a given context. This can be treated music specific span infilling denoising objective for sequence-to-sequene explicitly tell the model whether an input masked and the location of masked spans.
learning []6], but we don't t
B.3 Instrument Quantization
Band Arrangement While our tokenization scheme supports all MIDI program IDs, many instru- ments with different IDs share fundamental compositional properties, differing primarily in timbre (e.g., acoustic and electric pianos). To leverage these similarities and reduce instrument distribution sparsity in training data, during fine-tuning, group similar instruments and assign them the lowest program ID within their group. The instrument grouping rules follows that of [21. leading to 34 different program IDs in total. For multiple tracks of the same instrument type, merge their notes into a single track.
Piano Arrangement For piano arrangement, we consolidate all piano-type instruments (MIDI program IDs 0-7, including both acoustic and electric pianos) into a single track to form the target sequence.
B.4 Baseline Models
Band Arrangement For band arrangement, use the Transformer- VAE model from 42 as bascline. For human evaluation, we utilized their official implementation without retraining. The model's generation module trained on S1akh2100 [1] (the same dataset as fine-tu fine-tuning), and its prior model leveraged the larger Lakh MIDI Dataset (25) which encompasses Slakh2100 For objective evaluation, retrained another version of the model on Slakh2100 that did not receive hints from the style prior model for a fair comparison.
Piano Reduction Piano reductions can be categorized into two types: (1) piano accompaniments where the melody is delegated to a separate lead instrument, focusing solely on preserving harmony and texture (e.g., [41 (3)), which can be effectively handled using self-supervised methods; and (2) complete solo piano arrangements that additionally include the melody, requiring careful human arrangement and Band-to-piamy parallel datasets for supervised learning (c.g., (30 29). Due to the lack of open-source parallel datasets, we focus on the accompaniment arrangement task in this paper and use the term piano reduction interchangeably. However, our methodology is inherently flexible and can also handle the second type of reduction if parallel datasets become available. Additionally, exploring the potential for full solo piano reductions using unsupervised approaches remains valuable direction for future research.
For comparison, we reimplemented the UNet baseline from 30 and trained it on Slakh2100 using our data preparation pipeline. Instead of adopting parallel data, we followed the same setting as our model, using the original composition as input and the piano track within the song as output. To
15

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Table 7: Pre-train hyper-parameter setting.
Hyperparameters
Values
seed
42
learning rate weight_decay train batch_ssize size
0.0005
0.1
12
8
total trattrrbaicchh_hh_______ size
96
Adam with
optimizer
betas=(0.9, (099..99))
and epsilon=le-08
Ir_ jr_scheduler_ty
cosine 1000
Ir sechedueer_wrmmppe
num qum__poocss
1
ensure musical coverage, we selected only piano tracks that span more than 40% of the piece's ' pitch range.
Since hand-specific annotations (i.e., left and right hand separation) are unavailable, the entire piano track is generated single sequence rather than separate streams for each hand. We also omitted octave shifts during input preprocessing for two reasons. The first is that, unlike their setup where human-composed piano references may intentionally transpose notes by octaves, our output consistently corresponds to subset of the input notes without such shifts. The second is empirical: introducing octave shifts during training significantly degraded UNet's performance, reducing note-level F1 from 58.31 to 42.80 and diminishing perceptual quality.
Drum Arrangement For drum arrangement, we employ Composer's Assistant v2 19 as our baseline, specifically using v2. 1.0 from their official repository We use the model without retrain- ing since original training data (Lakh MIDI Dataset 25 encompasses our fine-tuning dataset
(Slakh2100).
Tokenization Comparison When comparing the effectiveness of REMI+ and the proposed REMI-Z tokenization in $3.2 the model structure used the same as our arrangement model, but with simpler training objective the standard left-to-right next-token tokee-prediction. We did not use time signature tokens, tempo tokens, and velocity tokens in REMI+ for a fair comparison.
B.5 Hyperparameter Settings
The model we adopted is GPT-2 model comprising 12 Transformer decoder layers with a hidden size 768. Our setup follows GPT's 12-layer, 768-dim embedding, 3072-dim inner states [24], and experimental design is also in the same style: a single pretrained model and multiple fine-tuning tasks to show the efficacy of the pretrain-firt finetune paradigm. We used a slightly higher number of attention heads (16 instead of 12) based the intuition that the interactions between different music tokens are more critical for musical quality than the value of individual note attributes. We detail the hyperparameter configurant configurations used in experiments below.
the pre-training configuration, where hyperparameters were used as-is without optimization. The pre-training was implemented using pytorch and transformers frameworks on a Linux platform, while fine-tuning additionally utilized lightning.
For pre-training, (ablee7ssmmes
For fine-tuning, we conducted simple learning rate search over le-5, 5e-5, le-4, selecting the optimal value based on validation loss. This resulted in learning rates of 5e-5 for drum arrangement and le-4 for band arrangement and piano reduction. The batch sizes and context lengths were configured as follows: band arrangement used batch size of and context length of 768; piano reduction similarly adopted batch size of 24 and context length of 768; drum arrangement employed a batch size and context length of 1536. Across all fine-tuning tasks, we used the Adam W optimizer with
http::////ggaaooooo////aaaaaaata awwpsttssssssssts-saaaaaaaaaaatt REAPER
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0.01 weight decay, incorporating a linear learning rate scheduler with 500-step warmup. Training spanned epochs for band, piano, and drum tasks, with early stopping patience of epochs. The best checkpoints were selected based on validation loss.
For the tokenization comparison, the model is trained from scratch without generative pre-training. The hyperparameter settings remain the same as those used for band arrangement fine-tuning, except for increased number of epochs (5).
All experiments were conducted with a fixed random seed of 42.
C Objective Metrics
In this section, we formally define the objective metrics used in the paper. All objective metrics calculated at the segment level, and arithmetically averaged across the test set.
C.1 Arrangement Evaluation
Most of our objective metrics based note-level F1 scores calculated piano roll of 16-th note quantization. Given the extreme sparsity of note events in the track-wise piano roll (e.g.. only O. 12% non-zero elements in the Slakh2 dataset under 16th-note quantization), Fl-based metrics are more suitable than accuracy-based metrics for similarity evaluation. Let X = {..,,..,, Zn and Y [y....... Fm J be two sequences of note events, where each note event is defined as a tuple e = (t. p) with onset time t and pitch A note event E X is considered to match yj Y if and only if:
Ve, 1 < 6 and Pr; Py;
where 6, is the temporal tolerance threshold (set to one 16th-note duration in our evaluation).
Let M (X,Y) Y) denote the set of matched note pairs between X and Y. The Note F1 score is defined as:
M(X,V Y)
Precision
(3)
X
M(X, Y) Y
Recall
(4)
2 Precision Recall
Note F1
Precision + Recall
The Note; Fl extends this by considering instrument matching, where each note event becomes (t, with representing the instrument. The matching criterion becomes:
1t.. tw, < 8, and Pz. Py, and ig. in
(6)
Note; F1 is then computed using Equation 5 with this stricter matching criterion.
For Melody FI (Mel F1), given multitrack piece with tracks T {Ti,... ..., Te), we first identify the melody track Tm as:
Σ
Tm = arg max T,ET
T
cET:
where [[]] is the number of notes in track T; and Pa is the pitch of note event e. The Mel Fl score between output and target sequences is then:
Mel FI = Note Fl (Trou Tigt
(8)
For instrument control evaluation, Instrument IoU (I-IUU) measures the overlap between instrument sets. Let out and Tl denote the sets of instruments used in the output and target sequences respectively:
Tout n It
I-IoU
Tout Ttgt
where instrument is considered present if there exists least note event using it.
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Finally, Voice Error Rate (VER) evaluates the similarity of voice arrangements between two multitrack compositions. For each piece, we derive an ordered voice sequence by:
1) Computing the average pitch p; for each active instrument i E Toctive;
1
P
Σ CET:
Pe
(10)
T
2) Constructing a voice sequence V by sorting instruments by descending average pitch:
V
[11 f2 ...: in] where pi, Piu+: for k 1,..., n2 1
(11)
The VER between output sequence Vour and target sequence Vigit is:
VER S+D+I N
(12)
where S, D. and I are the minimum number of substitutions, deletions, and insertions required to transform yout into Vtst and N ||/*** is the length of the Vige
Among all the selected objective metrics, the Note; FI the most comprehensive and important one. To achieve perfect Note, F1 score, the model need perform perfectly in all evaluated aspects: instruments, voice, melody, note reconstruction and allocation.
C.2 Tokenization Evaluation
Average tokens per bar (Thar) and average tokens per note (Tnote) are computed by first counting the total number of tokens and the number of bars notes, respectively, across the entire dataset. Specifically, Ter is obtained by dividing the total number of tokens by the number of bars, while Toote is obtained by dividing the total number of tokens by the number of notes.
Bar-level Shannon Entropy (Harr is computed by measuring the Shannon entropy of each bar-level token sequence (X):
H(X) = Σ P(z:) log, P(x:).
(13)
where X is the token distribution within bar. Then we average it across all bars in the dataset to obtain Hour-
Note-level Perplexity (PPLnote) aggregates probabilities over all tokens representing musical note (e.g., instrument, pitch, position, duration), and normalizes by the number of notes:
M
log P(nj mis-i)
PPI PPL mote
exp
(14)
J=1
where M the number of notes in a bar.
Finally, token-level Perplexity (PPLiokea) measures the standard autoregressive perplexity over all tokens. While commonly reported in language modeling, serves as an auxiliary metric here, does not directly reflect the model's ability model note events, which are basic units of music.
D Subjective Evaluation
D.1 Subjective Metries
These metrics are based on listeners' auditory experiences and their subjective feelings while listening the music. Since they are subjective. they cannot be easily defined using mathematical equations. However, we detail the evaluation criteria through the prompt questions provided in the questionnaire, which participants answered after listening the demos. Each metric assessed on a 5-point scale, ranging from I (very low) to 5 (very high).
Coherence: Does the arrangement flow naturally and smoothly? How consistent each instrument's performance and style throughout the piece?
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Creativity: How creative is the arrangement while maintaining faithfulness and naturalness?
Musicality: What is the overall musical quality?
Faithfulness (band): How closely does the arrangement resemble the original piece in terms melody and overall feel?
Faithfulness (piano): How closely does the arrangement capture the overall feel of the original piece?
Instrumentation (band-only): Does each instrument fulfill its appropriate role within the band, and do they harmonize effectively?
Playability (piano-only): How well is the piece suited for piano? How likely is it that human pianist could perform this accompaniment?
Compatibility (drum-only): Is the drum beat compatible with the other instruments?
Phrase Transition (drum-only): How effectively does the drum arrangement handle transi- tions between phrases?
D.2 Human Evaluation Details
Survey Design. We conducted subjective evaluations using audio clips arranged by different models across three tasks: band arrangement, piano reduction, and drum arrangement. For band arrangement, we used out-of-domall test songs with novel compositions and instrument groups; for the other two tasks, test set songs were used allow comparison with the original piano (Rule-O) or drum (ground truth) tracks. Each model was tasked with arranging the full song, and we selected a 15-30 second chorus phrase- the most representative segment- for evaluation. total, we prepared 6 songs for band arrangement, 5 for piano reduction, and 5 for drum arrangement.
Each sample group includes the original music to be arranged and the anonymized outputs from all compared models (plus the ground truth for the drum task). Each sample is 8-16 bars long, rendered audio in Cubase AI 13 using the default soundfont and the original MIDI's BPM. We collected 56, 73, and 77 evaluation groups for band, piano, and drum tasks respectively. The mean time spent per session was approximately 30 minutes. Figure shows the sample survey interface and instructions.
Participant Background A total of 26 participants joined the study, 5 of whom work in the music industry. Among all evaluators, 73.1% 1% have over 10 years of experience in music composition or performance. We conducted t-tests comparing these raters with those having < 10 years of experience (using piano reduction ratings on our model), and found no significant differences in the means between the two groups (p > 0.2 across all metrics). This suggests that musical background did not systematically bias the results.
Participants first listened to the original music clip, followed by model outputs presented in random order. For each arranged clip, participants rated multiple aspects using 5 5-point Likert scale, based on task-specific questions in the previous subsection. The evaluation groups randomly distributed to participants to ensure unbiased feedback. Each rater evaluated subset of groups, where one group one song (input) with multiple model outputs. On average, raters evaluated 15 groups for band, 2.80 for piano, and 2.96 for drum corresponding to 8.6, 14.0, and 11 11.8 samples respectively.
Evaluation Protocol.
Band Arrangement Settings. To evaluate generalization, designed three distinct instrumentation settings: (I) string trio (violin, viola, cello), (2) rock band (synth lead, clean electric guitar, distorted electric guitar, electric bass), and (3) jazz band (saxophone, violin, brass section, clean electric guitar, piano, string ensemble, electric bass). Each setting was applied to two different songs, covering six songs in total. The input content was drawn from both piano arrangements (3 songs) and band arrangements (3 songs), testing the model's ability to adapt to varying content and target instrument groups.
D.3 Discussion on Evaluation Scheme
Our experiments primarily adopt 5-point scale for subjective evaluation, which is the most commonly used protocol in prior music arrangement works [2 35. (8). However, we observe a growing trend
toward using A/B testing to evaluate overall quality in the broader music generation literature [32
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present arngeener emanged ascompaniments
The
each sample with following orieeia Pleose evllute lowast qualit)
with Please
comple
following entere (1-5 higheat lowest qualitg):
faitfalness
Coherence
flow
smooth,
Cohenence: Does amaegement ranurily and feels smooth? Hew consinent
drummer?
performance
trouughot anargement?
Phrase Transiton How well does the drum anangement handle sunsition between phrasee?
Playabiiity How liseyy this is piece composed for plane? How lieey human planar can play thes accompaniment?
Creativity How creative is the amangement wi thout loss neturainesst (HOTe count cnuatvyy)
Creativity: How creative the amangement without loss fainfalhees
prceette shun sotes
Musicalit: musical quality
Musicality Ovenal quality.
Amangement
Arangement
Please evaluate
Please
Comparbiiity
0
0
Fathhlnness
Coherence
Coherence
Phrase Transiton
0
0
0
0
0
Plyability
0
0
0
Muscaitt
0
0
0
Musicaltty
(a) An example of the piano reduction task.
(h) An example of drum arrangement task.
Figure 6: Screenshots of survey pages and instructions of our online survey.
23 37 We encourage future research consider combining both approaches to obtain more comprehensive and convincing evaluation results.
E
Probing Analysis of Pre-Traine Pre-Training Impact
Knowledge probing techniques are used to explore what a model has learned within its hidden representations (27. A widely adopted method is linear probing, where the pre-trained model is frozen and simple linear classifier trained on top of the hidden representations to predict specific properties, thereby revealing the extent which particular knowledge is encoded in the model [I] This technique is particularly useful in our context for evaluating what the model has internalized during pre-training.
have shown in the paper that the proposed models outperform the baseline models that do not undergo the pre-traning stage on the generation quality. In this section, we further analyze the reason. Specifically, we conduct probing experiments to assess whether pre-training enhances the acquisition of musical knowledge to facilitate understanding content conditions in fine-tuning.
We focus on two probing tasks: (1) classifying instrument types from content sequences that contain only position, pitch, and duration tokens, and (2) recognizing chord progression sequences from content sequences. determine whether instrument chord information is linearly accessible within the model's sequence embeddings, we employ linear classifier probes. In these probing experiments, average pooling the Transformer's output embeddings across all tokens in a sequence fed into the classifier. The model parameters are kept frozen, and only the linear classifiers are trained. We compare the knowledge captured by different models: randomly initialized Transformer, model that has undergone pre-training only (PT only), a model trained the band arrangement task
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Table Instrument probing results.
Model
Acc@1 I
Acc@ 3
Acc@5
Random guess
2.94
8.82
14.71
Random initialized
38.98
61.71
74.22
FT only
41.50
64.93
76.53
PT only
46.14
69.30
79.61
PT + FT
45.89
68.96
79.47
Table 9: Chord probing results.
Chord Root
Chord quality
Model
Acc@1 1
Acc@3
Acc@1
Acc@3
Random guess
8.33
25.00
11.11
33.33
Random initialized
48.86
78.76
38.05
77.97
FT only
50.09
80.23
39.26
79.03
PT only
62.93
89.42
50.09
85.58
PT + FT
58.05
85.93
44.92
82.48
without pre-training (FT only), and model that has undergone both pre-training and fine-tuning (PT + FT).
For the probing experiments, the batch size was set to 12 for chord probing and 64 for instrument probing. The learning rates were 5e-4 for chord probing and le-4 for instrument probing. Both experiments shared the same remaining hyperparameters: training for 10 epochs, using a linear learning rate scheduler, a 500-step warmup, and weight decay of 0.01.
E.1 Instrument Type Probing
In this task, we use a linear probe to estimate the instrument type from a single-track music sequence without providing instrument tokens. The goal is to predict which instrument is most likely to play the given note sequence. The performance evaluated using top-1, top-3, and top-5 prediction accuracy metrics.
As shown in Table 8 model initialized with random weights shows notable improvement after fine-tuning, suggesting that the ability to discern instrument styles is the requirement for performing well in music arrangement tasks. However, the gains in accuracy are modest, likely due to the limited number of training samples, which may constrain the model's ability to aquire such knowledge through fine-tue tetettunnng alone. Interestingly, models that underwent pre-training exhibit the highest accuracy in predicting instrument types. This indicates that substantial knowledge of instrument styles can be acquired effectively during the pre-training phase. Moreover, the proposed models that are both pre-trained and fine-tuned (PT+FT) maintained high accuracy levels, demonstrating that the knowledge about instruments styles are useful for the arrangement task.
E.2 Chord Progression Probing
In this task, we use linear probes to predict chord progressions from a 2-bar music content sequence without instrument tokens. Eight linear probes are trained simultancously to predict the chord roots and qualities for a total of four chords (two chords per bar). This tasks is used to evaluate whether the model contains position-specific chord information. Similarly, we use top-1 and top-3 prediction acuracy metrics.
As shown in Table the model with only fine-tuning (FT only) indicates foundational grasp of chord knowledge this complex music analysis tasks. However, similar to instrument prediction, the knowledge of recognizing chord progression does not significant gain, until the pre-training also introduced into the model, confirming that pre-training establishes robust basis for better understanding of content sequence, which may potentially help with the quality of arrangement.
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F
Broader Impact
This work presents a unified framework for symbolic music arrangement using pretrained generative models, achieved by a reconstruction fine-tuning objective and a structured tokenization scheme. By enabling high-quality arrangement generation under flexible control, our approach has the potential to make music creation more accessible to non-experts, reduce the technical burden for composers, and support educational and assistive tools for learning music theory, orchestration, or instrumentation. may also benefit creative professionals by streamlining workflows in game audio, film scoring, and digital content production.
Many other conditional generation tasks could similarly benefit from this training objective design. For instance, one can construct conditional sequence derived from a partial or aspect-wise decomposition of the target, concatenate it with the original target tokens, and then perform segment-level generation using a pretrained model with historical context-awareness to fine-tune existing generative models. Examples include bar-level infilling (e.g., removing and rewriting one bar), melody generation conditioned on chords, variation generation from a given melody, counter-melody generation, or harmonizing melody with chords. All of these tasks share the same musie-conditioned generation pattern and may benefit from a strong generative pretrained model.
Beyond arrangement tasks, the proposed tokenization scheme may have broader implications for symbolic music modeling general. By restructuring musical data into continuous track sequences, it facilitatees learning over reusable musical phrases. This may improve both conditional and un- conditional generation quality across tasks such as continuation, accompaniment generation, and reharmonization. Moreover, we anticipate that structured representations like REMI-Z could benefit music understanding tasks, including symbolic transcription from audio, by facilitating the model- ing of instrument-wise playing styles during decoding. Additionally, one may consider applying sequence compression techniques- such as Byte Pair Encoding (BPE) or variational autoencoders (VAE)-on top of this base tokenization to enhance compactness, which could further support long-range modeling essential for full-song generation. We encourage future work to explore these directions.
Beyond arrangement tasks, the proposed tokenization scheme may have broader implications for symbolic music modeling in general. By restructuring musical data into locally coherent, instrument- consistent sequences, it facilitates learning over meaningful musical units. This could improve both conditional and unconditional generation quality across tasks such as continuation, accompaniment generation, or reharmonization. Moreover, we anticipate that structured representations like REMI-Z may benefit music understanding tasks, including symbolic transcription from audio, by facilitating modeling of instrument-we playing styles when decoding. Also, you can consider combine sequence compression techniques, maybe BPE, maybe VAE, on top of that, to further enhance the compactness, which may facilitate long-range modeling that are important to full-song generation. We encourage future work explore these directions.
However, there are potential risks. As with other generative models, the misuse of automatic arrangement systems could devalue human artistry if deployed without appropriate attribution or transparency. The system may also reflect and amplify stylistic biases present the training data, which primarily consists of western popular music. This could marginalize underrepresented musical traditions or reinforce narrow definitions of musical aesthetics. We encourage future researchers and practitioners to consider ethical deployment strategies, such transparent model labeling, dataset diversification, and collaborative workflows where AI augments rather than replaces human creativity.
G Limitations
Fine-tuning dataset focused on pop genres. While our framework is designed to be genre-agnostic, our fine-tuning and evaluation primarily focus on pop-style arrangements. Future work can further explore the generalizability across a wider range of genres such as classical, jazz, non-Wor Western traditions.
Strict structural alignment assumption. Our framework assumes that the input and output music share identical musical structure, which enables While effective, this simplification does not fully reflect the broader musicological concept of
reconstruction formulation.
22

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arrangement, which often involves modifying the musical form or phrase structure. This constraint is not unique our method most prior works also rely on such structural alignment- but relaxing this assumption remains an important direction for future research.
Lack of instrument planning. Although system allows
to specify the instruments and their voice relationships for each segment during inference, the global planning of these assignments not addressed. In practice, achieving musically satisfying arrangement often requires deliber- ate instrument planning across segments-e.g, rotating lead instruments, or changing instrument combinations between sections. This work focuses on controllability at the segment level and leaves high-level planning strategies for future exploration.
Melody retention in piano reduction is not guaranteed. While piano reduction method captures harmonies and textures from ensemble music, it often fails to retain the melody. This is largely because the original piano track in multitrack data typically does not carry the main melodic line, and our current training setup does not include any explicit melody supervision. Solving this may require incorporating human-created piano arrangements or explicitly modeling melodic salience, which leave for future work.
Handling of Time Signature and Tempo. Our current model does not explicitly model time signature or tempo tokens during fine-tuning. As a result, it is limited to handling music with a fixed 4/4 time signature and stable tempo. This design choice follows common practice in prior arrangement research 442 35 30), where similar assumptions are made to simplify modeling. Empirically, approximately 95% of the fine-tuning data is in 4/4 time (see Appendix A and stable tempo is generally the norm in modern music 7 Nevertheless, extending the framework to handle non-4/4 time signatures and dynamic tempo variations remains an important direction for future work.
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Reviews:
[{"review_id": "vAH2HbW5uG", "reviewer": "Reviewer_Xe8N", "summary": "This paper proposes a method for automatic multitrack music arrangement. The authors aim to integrate different aspects of this task—band arrangement, piano reduction, and drum arrangement—into a single unified model. Recognizing the limitations of strictly time-ordered tokenization schemes such as REMI+, the authors introduce **REMI-z**, a structured tokenization approach that organizes musical notes according to pitch and instrument. They also propose a unified fine-tuning method on the REMI-z sequences. Experimental results demonstrate that this method outperforms existing approaches across all three tasks, highlighting the effectiveness of both the REMI-z encoding and the fine-tuning strategy.\n\nIn summary, this work presents two key contributions to multitrack music arrangement: a novel encoding scheme and a unified fine-tuning method.", "questions": "See weaknesses and suggestions.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n1. The paper presents a valuable motivation by proposing a unified model for diverse music arrangement tasks.\n\n2. The authors provide clear descriptions of dataset construction, encoding examples, and relevant implementation details, ensuring full reproducibility.\n\n3. The paper maintains strong alignment between its methodology and experiments. All three tasks, the fine-tuning components, and the new encoding scheme are carefully evaluated, with consistent and convincing improvements shown.\n\nWeaknesses\n\n1. **Unclear Method Description**: The definition of the task-specific source sequence is unclear when it is first introduced. Moreover, it remains ambiguous how the proposed method could be generalized to other music-related tasks beyond the three examined here. (See suggestions below for more detail.)\n\n2. **Evaluation Concerns**: The three evaluation tasks appear to be newly defined by the authors without references to prior work or benchmarks. In particular, for the *Band Arrangement* task, if the model is provided with $y$ as $S_{\\text{task}}(y)$, the task may become trivial—even with randomness introduced during training—since the model can directly refer to the source and assign corresponding instruments. This could explain why it outperforms methods like VAE on note-F1, which encode source information into latent vectors. Therefore, I remain skeptical about whether it is reasonable to include target note information directly in the input during evaluation.\n\nSuggestions\n\n1. On line 48, where you state *“We further observe that strictly time-ordered tokenizations introduce redundancy and fragment track content,”* I suggest adding supporting evidence—either references or quantitative/visualized examples. Readers may not be fully convinced why REMI+ is unsuitable here; simply stating that it is “unstructured” is insufficient, since REMI+ is time-structured by design. This clarification is especially crucial because it supports your motivation.\n\n2. Please provide a clearer explanation of the task-specific source and target tracks around lines 145–146. From Figure 2, it appears that *CONTENT* is merely a rearrangement of the *TARGET SEQUENCE* without instrument labels, making it confusing why the answer is embedded in the input. Only in Section 3.3 does it become clearer that the source can take other forms. It would greatly improve readability if you clearly defined this source-target relationship at the first mention—especially since you aim to build a unified framework. You might also consider reorganizing Sections 3.1 and 3.2; presenting the training method before the tasks could improve the logical flow, as the training process directly depends on task formulations, while REMI-z could naturally follow afterward as the core technical detail.", "was_revised": false, "final_justification": "", "rebuttal": "First of all, thank you for your comments. We appreciate your recognition of our core contribution, and your suggestions are very constructive. Below, we address the specific concerns you raised.\n\n--------------------------------------------\n\n**Weakness 1: The definition of the task-specific source sequence is unclear at first introduction, and how can the method generalize to other tasks**\n\nYou're absolutely right. Although the formula for the task-specific source sequence appears in Section 3.1, we don’t actually explain what $S_{\\text{task}}$ and $T_{\\text{task}}$ mean until Section 3.3, where we introduce the individual tasks. In the next version, we’ll briefly clarify right after Eq (1) what these two filters are and why they’re necessary — specifically, that not all tasks require all tracks as conditions or outputs, and that the exact sets depend on the needs of each task.\n\nAs for generalization, we’d like to clarify that the central focus of this paper is still **arrangement** — that is, creating music variations for different instrument settings — and we selected three representative tasks to cover different scenarios in this domain. Generalizing to other music-to-music generation tasks is definitely part of our long-term goal. Many such tasks can be formulated similarly: we construct a conditional sequence (often derived from a partial or aspect-wise decomposition of the target), concatenate it with the remaining target tokens, and then apply segment-level generation using a pretrained model with context-awareness from the history. REMI-z may also contribute to performance improvements in these broader scenarios.\n\nExamples of such tasks include bar-level infilling (e.g., removing and rewriting one bar), melody generation conditioned on chords, variation generation from a given melody, counter-melody generation, or harmonizing a melody with chords. These all share the same music-conditioned generation pattern. Due to time constraints, we only conducted experiments and evaluations on the three most representative arrangement cases. In the current version, we only hint at broader applicability in the abstract and conclusion using intentionally soft phrasing (\"suggest\", \"potential\"). Actually, we have also explored finer-grained control in a separate setting, where each instrument’s texture (i.e., playing style) is treated as a condition during self-supervised training to control generation behavior per track. The results were encouraging, but we chose not to include them here due to limitations in focus, space, and time. In the next version, we will discuss the potential generalizability of our method in depth in Appendix F to avoid potential confusion. \n\n\n**Weakness 2: Evaluation tasks are newly defined and may be trivial, especially for band arrangement where target note information is seemingly present in the input**\n\nThank you for raising this concern — we acknowledge that our current writing didn’t cite the task origins explicitly. In fact, the three tasks we use are based on prior work: band arrangement follows the setup in [1,2] with a more challenging setting (without track-wise prior as prompt), piano reduction is adapted from [3,4], and drum generation serves as a representative case of track-level infilling, inspired by [5,6]. We’ll make sure to include these references in the task descriptions in the next version.\n\nRegarding the objective evaluation setup for band arrangement, we believe the current input format is proper and follows the previous works [1,2], where it is common to provide note event information without instrument labels, and have the model infer an instrument-specific arrangement from this content. In our notation, the input is not $S(y)$, but rather $C(S(y))$. The $C()$ function removes all instrument identity, imposes a strictly time-ordered view (so that note ordering doesn’t implicitly encode track information as well), removes duplicate events, and even removes note duration. As shown in Figure 5 of Appendix A, applying $C()$ significantly alters the appearance of the target sequence — making the task non-trivial.\n\nRecovering $y$ from $C(y)$ is far from easy. In music theory, this task is often referred to as instrumentation or orchestration [7]. It requires extensive knowledge of instrument properties, arrangement principles, and often involves collaboration with musicians who are familiar with specific instruments. For a model, random allocation would clearly fail, and naive solutions like rule-based heuristics (Table 2) don’t perform well. A good model must learn not only each instrument’s physical constraints (e.g., playable pitch range), but also their typical role in an ensemble — whether melodic, harmonic padding, flourish, or rhythmic base — in order to produce high-quality arrangements. This objective setup implicitly evaluates whether the model understands these roles from training.\n\nWhile objective metrics may not perfectly correlate with perceived musical quality (that’s why we also conducted human evaluations to assess creativity and musicality), they are still meaningful. We believe the objective evaluation results here suggest that our model successfully captures instrument-role relationships with minimal external guidance — unlike [1], which requires note density and pitch histogram prompts, or [2], which relies on fixed instrument combinations throughout the training set.\n\nYour point is an excellent reminder that this setup may not be self-evident to readers unfamiliar with music theory. We will cite the papers related to the task settings, add a short paragraph in Section 3.3 to clarify the difficulty of the band arrangement task, and move the fuller explanation above into a new Appendix section.\n\nAs a minor clarification: there is no randomness in the objective evaluation for band arrangement (see L234); creativity only enters during human evaluation to avoid introducing noise in F1-based metrics.\n\n\n**Suggestion 1: The claim that time-ordered tokenizations introduce redundancy and fragment content lacks supporting evidence**\n\nThanks for the suggestion. In the Introduction, we prioritized logical flow and therefore didn’t go into detail at that point. However, the actual issue of content fragmentation — what it means and why it matters — is explained later in Section 2.2 (starting from line 99), along with Figure 3 as an illustrative example.\n\nIn the next version, we’ll explicitly refer readers to Section 2.2 when we first mention this issue, to help avoid confusion and ensure the motivation is clearly supported.\n\n\n**Suggestion 2: Clarify the source- and target-specific tracks and consider reorganizing Sections 3.1 and 3.2**\n\nThank you for pointing out this potential source of confusion. This is indeed closely related to our response to Weakness 2. The “content sequence” is, as you observed, a kind of “rearrangement” by flattening all note events to a single time-ordered stream — and learning how to reconstruct the full original music from it is exactly what our model is designed to do. This provides a self-supervised formulation of the arrangement task, which, as mentioned earlier, is far from trivial. In music theory, this kind of reconstruction requires significant domain expertise, and existing models may struggle with it. We’ll make sure to emphasize the difficulty of this task more clearly in Section 3.3, and include a more detailed explanation in a new Appendix section (since the main text may not have enough room).\n\nRegarding the ordering of sections — we think your suggestion makes a lot of sense. While $S_{\\text{task}}$ and $T_{\\text{task}}$ are introduced formally in Section 3.3, they first appear in Section 3.1. In the next version, we will explain their meaning and necessity at first mention. We also plan to restructure Section 3.1 to present the training method and Equation (1) earlier, followed by the different task-specific conditioning schemes. We believe this will significantly improve readability and logical flow.\n\n\n\n--------------------------------------------\n\n\nThat concludes our response — thank you again for the thoughtful and constructive comments!\n\nTo summarize, we realize we may not have conveyed the full complexity of the band arrangement task as clearly as we could have, and we’d be happy to revise and expand on this in the camera-ready version (if given the opportunity).\n\nIf you feel the clarifications helped address your concerns, we’d be truly grateful if you’d consider a slightly more positive rating. It would mean a lot to us. Thank you again for your time and support!\n\n\n\n[1] Zhao, J., Xia, G., Wang, Z. and Wang, Y., 2024. Structured multi-track accompaniment arrangement via style prior modelling. Advances in Neural Information Processing Systems, 37, pp.102197-102228.  \n[2] Dong, H.W., Donahue, C., Berg-Kirkpatrick, T. and McAuley, J., 2021. Towards automatic instrumentation by learning to separate parts in symbolic multitrack music. arXiv preprint arXiv:2107.05916.  \n[3] Terao, M., Nakamura, E. and Yoshii, K., 2023, June. Neural band-to-piano score arrangement with stepless difficulty control. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.  \n[4] Chiu, S.C., Shan, M.K. and Huang, J.L., 2009, December. Automatic system for the arrangement of piano reductions. In 2009 11th IEEE International Symposium on Multimedia (pp. 459-464). IEEE.  \n[5] Malandro, M.E., 2023. Composer's Assistant: An Interactive Transformer for Multi-Track MIDI Infilling. arXiv preprint arXiv:2301.12525.  \n[6] Malandro, M.E., 2024. Composer's Assistant 2: Interactive Multi-Track MIDI Infilling with Fine-Grained User Control. arXiv preprint arXiv:2407.14700.  \n[7] Wikipedia. 2024. *Orchestration*. [online] Available at: <https://en.wikipedia.org/wiki/Orchestration> [Accessed 25 Jul. 2025]."}, {"review_id": "B7HnNKFPeE", "reviewer": "Reviewer_SnRL", "summary": "\"Unifying Symbolic Music Arrangement: Track-Aware Reconstruction and Structured Tokenization\" presents a new method for symbolic music tokenization, building on ideas from REMI+. Models trained using REMI-z are compared to existing baselines on 3 musical tasks, across a wide suite of qualitative and quantitative evaluations. Across nearly all task metrics REMI-z shows significant gains, and musical examples also show the efficacy of this tokenization scheme.", "questions": "One key piece to proposing new tokenizations, can be showing their application at a variety of scales in more than one baseline model type - here it seems one, relatively small but high performing model architecture is all that was trained along with the complementary \"no PT\" model for each task. Are there any additional ablation studies at other model scales? Or integration into another existing model, to show it also generalizes well to this new tokenization? Transformers are very general, but given one of the comparison models is a UNet, can this tokenization also be applied to UNets and other non-Transformer models?\n\nI did not see any direct generation results in the samples page, only other applications like reductions and arrangement. Do the authors have any generative samples to show, to get some idea of how this tokenization works for pure generative applications?\n\nBar encoding and adding in bar/timing marks can be a difficult endeavor - did the authors do anything special on certain datasets for cleanup and addition of these features? Or did all datasets have sufficient bar marking and note timing / quantization to apply REMI-z without additional preprocessing?\n\nThere is some comparison to TransformerVAE, but have the authors done any comparison to a learned encoder and quantization (similar to many common methods in multimodal modeling) using a frozen model with quantized output as a fixed tokenizer, directly in the same setting as the model training? This is mostly a curiosity question about learned structure, versus human specified.\n\nDid the authors investigate instruction style finetuning at all (besides drum arrangement, which is kind of instruction-like)? Finding a melody and harmony to match a drum backing, laying a new melody over an existing harmonic structure, or harmonizing existing melody lines would be some other examples of useful \"instruction tuning\" for music tasks.\n\nParticularly it seems there are significantly more tasks than raters in the rating pool - do the raters with musical expertise have a fundamentally different scoring than amateur / non-musical folks? Did every rater rate every single example, or what was the average amount of examples rated per rater?\n\nOn this line \"... indicates reduced information redundancy, and suggests that REMI-z sequences consist of more predictable tokens, potentially facilitating the learning of generative models\". Couldn't this also mean *more* redundant representations (for example, repeated tokens that may be merged in other tokenizers like BPE), rather than less redundant. There is often a fundamental tradeoff of compression/redundancy versus predictability, and it may be that less predicatable representations facilitate harder learning tasks, and thus better models in terms of robustness e.g. dropout, track deletion, and so on, so more specific measurement of this point and qualitative analysis would help if this is a critical and key point the authors wish to make.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\nThe writing is both detailed and clear, with strong organization. The method is straightforward and pragmatic, with thorough appendix content. I will write much more on weaknesses than strengths, but that is because the paper and approach are generally well-rounded! It is hard to comment on the strengths, because overall this paper is good, lacking only in a few small-but-critical to improve areas.\n\nWeaknesses:\nThere are many, many metrics in the paper, but it is difficult to determine which are key. I lean toward direct human ratings first, for many of these tasks but human and quantitative metrics are mostly interleaved throughout the paper. There are human ratings across 4 separate sub-areas, where the model is ahead by a statistically significant amount in all categories - but are the overall ratings also positive? For example, there is a dimension for musicality but this may not align with \"enjoyment\" of the piece - some discussion of this would be useful. I would expect this \"global score\" to also be positive, but means a simple A/B head to head would be of interest as well. For the ratings, some description of the expected average musical ability of the raters (beyond just the music industry mention - this is not specific enough with regards to musical ability) used for the ratings would be useful to contextualize rating numbers.\n\nAdditional baselines would be useful - TransformerVAE and the UNet comparison are pretty limited in terms of comparisons, especially given the clear losses to the proposed method. Seeking out stronger baselines and more points of comparisons, given the broad flexibility of this tokenization and model, would help me boost my score - as the limited external points of comparison, along with only having 2 task models (w and w/o PT) limits the impact of the many metrics and human ratings performed. \n\nFigure 1 is a bit hard to read, and going directly into tasks right away doesn't draw a reader in. It might be better to consider the tokenization figure (figure 3) as a better introduction, or even figure 5, as that is what the technical contribution of the paper is about - the tasks are a test of the technical contribution and model. Alternatively, some diagram showing the training and finetuning stages, the lifecycle of the model training, leading into Task 1, and so on, is also worth consideration.\n\nGiven the use of track-level deletion (which seems important, and likely important enough to move out of the appendix), some discussion and citation of related work like \"Efficient Training of Language Models to Fill in the Middle (FIM)\", BERT, Masked Autoencoder (MAE), discrete diffusion, and many other span-based methods in the background section could be worthwhile. Showing the *failure* when not doing this, would also be an interesting qualitative study.", "was_revised": true, "final_justification": "Though I take the author's point that the contribution is not solely around tokenization, the title itself ends with \"Structured Tokenization\" and there are only limited model ablations directly (in terms of model scales / design variations). However, they have shows extensive ablations across tasks, and the writing is overall quite clear and fundamentally I like the paper. I have raised my score 1 point around this issue, as I do think this paper is something of interest in the symbolic music generation community - and will communicate what I think could make this work stronger still based on the first round of rebuttals.", "rebuttal": "Thank you for your thoughtful and constructive feedback! We sincerely appreciate your kind words on our tokenization design. Your comment that “the paper and approach are generally well-rounded” truly made our day. Below we address your concerns in detail.\n\n---\n\n**Clarifying the paper’s scope: The review’s “Summary” appears to primarily frame the contribution around tokenization.**\n\nWhile REMI-z is indeed an important component, we would like to clarify that the paper is centered on **symbolic music arrangement**, as reflected in the title and the Introduction section.\n\nOur key contributions are:  \n(1) a unified model design across diverse arrangement tasks;  \n(2) a self-supervised fine-tuning objective for arrangement (Sec. 3.1);  \n(3) a track-aware tokenization scheme (REMI-z) that facilitates modeling (Sec. 3.2);  \n(4) strong performance across three representative and challenging arrangement tasks.\n\nIt’s possible that we did not sufficiently highlight the second contribution — the arrangement-specific fine-tuning objective — which is technically substantial and complementary to the tokenization. We’ll make sure to emphasize this more clearly in the Introduction of the next revision.\n\nWe hope this clarification helps better contextualize our responses below. \n\n\n\n**Weakness 1: Relative importance of metrics, and the overall rating are unclear**\n\nWe agree the rationale behind metric choices could be more explicit. For example, `Note_i F1` is the most critical, as it directly reflects closeness to ground-truth compositions [17,18]. We’ll clarify the metrics’ priorities and their motivations in Appendix C.\n\nAs for the “overall rating,” the `Musicality` metric plays this role since the prompt asked “What is the overall musical quality?” (L661). In all three tasks, our model achieved the highest `Musicality` and was significantly preferred over major baselines.\n\nWe agree A/B testing is valuable, but 5-point rating protocols are widely used in prior arrangement works [39,32,35]. Moreover, our results show strong significance (most p < 0.001), indicating clear improvement. We’ll include a new Section D.3 in the appendix to discuss the benefit of A/B testing and suggest it as future work.\n\nLastly, thanks for flagging the vague “music industry” label. In the final version, we’ll clarify rater backgrounds (e.g., average years of training) to better contextualize the subjective results.\n\n\n**Weakness 2: More (stronger) external baselines would be useful**\n\nThanks for the suggestion — though this may stem from a misunderstanding of our paper’s main contribution. Our focus is symbolic *music arrangement*, and the baselines we compare with are (to our knowledge) the strongest, task-specific SOTA methods: TransformerVAE [39] for band, UNet [26] for piano, and ComposersAssistant 2 [17] for drum. We believe this baseline selection is appropriate.\n\nIf the paper were about proposing a general-purpose tokenizer, then broader task selections would be needed. But REMI-z is a supporting tool to enhance arrangement, but not the paper’s focus. Our goal is a unified, practical solution for diverse arrangement tasks.\n\nEach of the three tasks includes 4–5 arrangement candidates in human evaluation. The numbers of candidates are comparable to prior works like TransformerVAE [39], hence we believe the human evaluation is reliable. \n\nSince you noted this concern influenced your score, we hope this clarification helps. Our baseline choices were carefully matched to the scope and contribution of this work — advancing music arrangement via a unified model. We don’t claim generalization beyond that (e.g., to other model architectures or tasks). We hope this clarification resolves the concern — and we appreciate your consideration of our intent and scope.\n\n\n**Weakness 3: Figure 1 is a bit hard to read, consider replacing it.**\n\nWe agree that a lifecycle diagram showing pretraining, fine-tuning, and inference would help clarify the overall structure. We’ll add one before Figure 1 in the revised version to guide the reader through the stages of our method.\n\nBut we believe Figure 1 still serves a key purpose and should remain in place. It conveys the high-level unification that the same model and training strategy can address multiple arrangement tasks, which is the central message of the paper. \n\n\n**Weakness 4: Track-level deletion seems important, and discussion of span-based methods may be needed.**\n\nThe reason we placed this in the appendix is that track deletion is only used in the *band arrangement* task. We’re happy to add discussions of other span-based methods like BERT to the Related Work (or Appendix B.2 if cannot meet the space constraint) in the next version.\n\n\n**Q1: Additional test of REMI-z at different model scales or in alternative architectures**\n\n**On evaluation scope:**  \nThis paper is not a tokenization paper, but an application-driven work focused on improving symbolic music arrangement. Although we showed REMI-z has potential beyond arrangement tasks (Sec. 5.4), further exploration of it is beyond the scope of the paper.\n\n**On model scaling:**  \nSince our focus is on method, not scale, we deliberately use small models and modest-scale pretraining to show that improvements stem from the approach itself. Larger models generally yield better results, but replicating that trend may not strengthen our core contribution. \n\n**On what was trained :**  \nWe conducted pretraining, fine-tuning on 3 tasks (with/without pretraining), re-trained TransformerVAE and UNet, and additionally trained 1 conditional + 2 unconditional models for tokenization comparison. Other baselines were used as-is, with rationale in Appendix D.4. While some other works may report hundreds of experiments, our aim is to show that this unified setup outperforms strong task-specific systems — which we believe our experiments have sufficiently demonstrated.\n\n\n\n\n**Q2: Do the authors have “direct generation results”? Any special handling for bar/timing marks?**\n\nAll results on our demo page are *direct generation outputs* from our models and baselines, across all *arrangement tasks* mentioned in the paper. \n\nFor bar and timing encoding (Section 3.2, Appendix A): Bars are delimited by `End-of-Bar` tokens. Note onsets are encoded as relative positions within a bar using 48-th note quantization (supporting 16th-note triplets, same as REMI+). Durations are encoded similarly. This scheme applies uniformly across all datasets, without task-specific cleanup.\n\n\n**Q3: Have the authors tested learned quantization/tokenizer approaches?**\n\nWe haven’t tried with frozen encoders or codec models in this work. \n\nWe view REMI-z and learning-based quantization/tokenization as orthogonal (as L111). REMI-z is a symbolic-level base tokenization — it defines musical events with a token sequence. Learned quantization/tokenization (VQ-VAE, codec, or BPE) typically operates *on top of* such base sequences. But our focus is on improving base token structure to improve arrangement modeling. Combining REMI-z with learned quantizers/tokenization is possible, but outside this paper’s scope. We will clearly explain this in Sec. 2.2 in the next version.\n\n\n**Q4: Instruction finetuning, more tasks, and subjective evaluation details**\n\n**Instruction finetuning.**  \nWe agree this is a promising direction. We didn’t try it in this work because different tasks involve different segment lengths, which makes batching inefficient. \n\n**Applicability to more tasks.**  \nOur focus is on *arrangement*, specifically generating musically meaningful versions of a piece under new instrumentation. The three chosen tasks cover diverse and representative scenarios. While other generation tasks may be potentially supported, we chose to stay focused for clarity. We see general music-to-music generation as a promising extension.\n\n**Musical background of raters**  \n73.1% of raters reported >10 years of musical training (L685). We ran t-tests comparing these raters to those with ≤10 years (using piano reduction ratings on our model), and found no significant differences of the mean of the two clusters (*p* > 0.2 across all metrics). This suggests musical background didn’t systematically bias results. \n\nEach rater evaluated a subset of groups, where one group = one song (input) with multiple model outputs. On average, raters evaluated 2.15 groups for band, 2.80 for piano, and 2.96 for drum — corresponding to 8.6, 14.0, and 11.8 samples respectively (L683). We will add these details in Appendix D.2.\n\n\n**Q5: Each REMI-z token seems to carry more information and hence should be more redundant?**\n\nWe acknowledge that “redundancy” here was not precisely used. REMI-z has lower redundancy mainly due to its shorter sequence length, achieved without introducing new token types compared to REMI+. Meanwhile, its lower Shannon entropy indicates a more predictable token distribution. As a result, REMI-z uses fewer and more predictable tokens, making it potentially easier for generative models to learn — and validated by its lower note-level entropy. As noted in Q3, REMI-z is a base tokenizer and can be combined with BPE for further improvements. Since tokenization is not the paper’s focus, we stopped exploration at this point. We will revise Section 5.4 for clarity.\n\n\n---\n\nThat concludes our response — thank you again for the thoughtful and constructive comments!\n\nTo summarize, the focus of the paper is about a unified method for symbolic music arrangement, rather than a general-purpose tokenization scheme, and we believe this adequately justify our experiment designs. We’d be more than happy to revise the framing and emphasize the major contribution in the camera-ready version (if we’re fortunate enough to have the opportunity).\n\nIf you feel the clarifications helped address your concerns, we’d be truly grateful if you’d consider a slightly more positive rating. It would mean a lot to us. Thank you again for your time and support!"}, {"review_id": "mBxugmYFda", "reviewer": "Reviewer_FStY", "summary": "This paper introduces a unified framework for symbolic music orchestration, enabling flexible conversion from any input track to any target track via a “segment-level reconstruction” objective and a factorized “content–instrument–context” conditioning scheme. The key contributions are: (1) the REMI-z structured multi-track token representation, which maintains continuity within each instrument part and significantly reduces redundant tokens; (2) a lightweight fine-tuning approach on a pretrained symbolic music model that handles three representative orchestration tasks—band reinterpretation, piano simplification, and drum generation—without requiring parallel data or additional architectures; and (3) comprehensive evaluations on the Slakh2100 dataset showing that the proposed method outperforms task-specific state-of-the-art baselines in both objective metrics (Note F1, Instrument-IoU, Voice-WER, etc.) and subjective listening tests (fidelity, coherence, creativity, playability).", "questions": "1. How does REMI-z perform on segments with non-4/4 time signatures, mixed meters, or drastic tempo changes?\n\n2. Can the framework support finer user controls, such as specifying dynamics, timbral blending, or ornamentation styles?\n\n3. For full-song orchestration (beyond single segments), how can cross-segment consistency be maintained, and what designs might handle transitions between sections?", "limitations": "Reliance on symbolic MIDI input; it does not address errors or noise introduced by audio-to-MIDI conversion.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: A single model, after self-supervised fine-tuning, supports multiple orchestration scenarios without parallel data or specialized modules.\n\nWeaknesses: Fine-tuning only considers 4/4 time and ignores tempo and time-signature tokens, which may limit adaptability to unusual meters or tempo fluctuations.", "was_revised": true, "final_justification": "Thanks for the author's detailed response.\nBased on the rebuttal, I will maintain the score", "rebuttal": "First of all, thank you for your review. We appreciate your recognition of our core contribution — both the tokenization design and the fine-tuning formulation — and your questions were truly insightful. Below, we respond to your comments in detail.\n\n--------------------------------------------\n\n**Weakness: Fine-tuning only considers 4/4 time and ignores tempo and time-signature tokens, which may limit adaptability to unusual meters or tempo fluctuations**\n\nYou're absolutely right — our fine-tuning experiments only used 4/4 music, but there’s a clear rationale behind this. Many existing works on music arrangement [1,2,3] have also made the same assumption, and our choice follows this established convention. More practically, time signature follows an extremely long-tail distribution. As noted in Appendix A.1, about 95% of music in our fine-tuning dataset is in 4/4 — meaning only around 50 songs use a different time signature. This data size is probably insufficient for the model to learn to arrange for non-4/4 meters. Since our primary goal in this paper is to verify the viability of our proposed approach, we chose to focus on the most common scenario.\n\nYou're also correct that our fine-tuning setup did not model tempo fluctuations — in fact, we did not include tempo tokens in fine-tuning. This also follows convention in prior works [1,2,3]. While tempo changes do occur in modern music, they’re relatively uncommon, and stable tempo is generally the norm [4]. So although being able to handle tempo fluctuations would certainly be a nice-to-have feature, it was not prioritized in this work.\n\nBut it’s worth noting that our pretraining stage *does* use bar-level time signature and tempo tokens. So in principle, the model may have the potential to handle changes in time signature and tempo — if such information is also incorporated and with a larger scale fine-tuning. But since these factors are not central to the core problem of arrangement, we did not explore this direction in detail. We fully acknowledge this as a limitation and will include a discussion of it in Appendix G. This is certainly an open direction worth exploring in future work.\n\n\n**Question 1: How does REMI-z perform on segments with non-4/4 time signatures, mixed meters, or drastic tempo changes?**\n\nWe haven’t tested our model on such cases. As mentioned earlier, the fine-tuning data used in our experiments was almost entirely in 4/4 and didn’t include tempo fluctuations. So while the model was pretrained with time signature and tempo tokens, we didn’t incorporate that information during fine-tuning.\n\nBut it’s *likely* that REMI-z could handle such scenarios, especially with appropriate finetuning settings. Our time signature and tempo design follows MuseCoco [5], a model that has demonstrated the ability to handle varying time signatures and tempo changes (as shown on their demo page). So we’re optimistic that REMI-z might be capable of similar generalization, though confirming this would require further empirical validation — and we see that as an interesting direction for future work.\n\n\n\n**Question 2: Can the framework support finer user controls, such as specifying dynamics, timbral blending, or ornamentation styles?**\n\nIn theory, yes — if these aspects can be extracted from the target music and included as part of the condition during self-supervised training, the framework should be able to support them. Although finer grained control may not be a priority in existing works [1,2,3], We did conduct some initial experiments on finer-grained control. Specifically, we explored conditioning on each instrument’s playing style (or “texture”) as part of a self-supervised setup. This allowed us to control the generation behavior of each track with greater nuance. The results were promising, but due to focus, time constraints, and space limitations, we did not include them in this paper. We believe these directions are valuable and worth exploring in future work, and would like to add an additional section in Appendix to discuss potential possibilities to add finer-grained control.\n\n\n**Question 3: For full-song orchestration (beyond single segments), how can cross-segment consistency be maintained, and what designs might handle transitions between sections?**\n\nThis is a great question. Our observations differ depending on whether the arrangement involves pitched instruments (band arrangement and piano reduction) or drums.\n\nFor pitched instruments, we found in early experiments that even without an explicit history condition, when given similar content sequences and instrument constraints, a trained model tends to generate stylistically similar arrangements. So when a musical segment reappears (e.g., a repeated chorus), the model tends to rearrange it in a consistent style. The long-term coherence is hence preserved just because the original composition has that coherence. We showcase this in several of our demos. By contrast, short-term cohesion between adjacent but dissimilar segments is a more pressing challenge, and this is precisely why we introduced the history condition. Without specific design choices, the model may apply entirely different arrangement “ideas” to adjacent segments, leading to a noticeable sense of discontinuity. \n\nFor drum arrangement, since drum patterns are often more temporally dispersed, we used longer segments (4 bars). In this setting, the model appeared to learn how to detect and manage segment transitions on its own — a very interesting behavior. Increasing segment length may thus be another promising way to improve global coherence.\n\nWe believe these observations are quite interesting, and we plan to include a new Appendix section titled “Discussion” to elaborate on them in the next version.\n\n\n**Limitation: Reliance on symbolic MIDI input; it does not address errors or noise introduced by audio-to-MIDI conversion**\n\nThank you for pointing this out — we fully agree. Our work focuses on symbolic-level music arrangement and assumes clean MIDI input. Handling errors from upstream audio-to-MIDI conversion is beyond our current scope but is indeed an important practical consideration. We will add a note in the Limitation section to acknowledge this and encourage future research to explore multitrack arrangement in the audio-to-MIDI setting.\n\n\n\n--------------------------------------------\n\nThat concludes our response — thank you again for the thoughtful and constructive comments!\n\nTo summarize, we acknowledge that many finer aspects — such as time signature, tempo, and velocity — though not the center of the arrangement problem, were not addressed in the current version of the paper, but they are indeed meaningful and worth exploring. We’d be happy to include a more thorough discussion of these limitations in the camera-ready version (if given the opportunity).\n\nIf you feel the clarifications helped address your concerns, we’d be truly grateful if you’d consider a slightly more positive rating. It would mean a lot to us. Thank you again for your time and support!\n\n\n\n[1] Zhao, J., Xia, G., Wang, Z. and Wang, Y., 2024. Structured multi-track accompaniment arrangement via style prior modelling. Advances in Neural Information Processing Systems, 37, pp.102197-102228.  \n[2] Wang, Z., Xu, D., Xia, G. and Shan, Y., 2022, May. Audio-to-symbolic arrangement via cross-modal music representation learning. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 181-185). IEEE.  \n[3] Terao, M., Nakamura, E. and Yoshii, K., 2023, June. Neural band-to-piano score arrangement with stepless difficulty control. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.  \n[4] Dannenberg, R.B. and Mohan, S., 2011. Characterizing tempo change in musical performances. In ICMC."}]

Author rebuttal:

Meta-review: This paper proposes a unified symbolic music framework with a new tokenization scheme (REMI-z) and segment-level reconstruction objective, enabling a single pre-trained model to perform diverse multitrack music arrangement tasks and outperform task-specific baselines.

**Comments:**

- Reviewer Xe8N (Final Rating: 4 - Borderline accept): The paper makes a valuable contribution by presenting a unified framework with strong methodology–experiment alignment and reproducibility, though clarifications on task definitions and evaluation design would strengthen it.

- Reviewer SnRL (Final Rating: 5 - Accept): The work is clearly written, well-organized, and demonstrates thorough ablations and strong results, with the main suggestions being to add stronger baselines and clarify human evaluation.

- Reviewer FStY (Final Rating: 4 - Borderline accept): The model convincingly supports multiple orchestration scenarios with a single framework, with the only limitation being its current focus on 4/4 time and lack of tempo/time-signature handling.


**Meta Comments:**

The reviewers agree that the paper is well-written, reproducible, and presents a solid and pragmatic unified framework for multitrack music arrangement, showing consistent improvements and strong potential impact in the symbolic music community. While concerns remain about limited baselines, unclear task definitions, scope of evaluation, and some missing aspects of musical representation (e.g., time signatures, tempo), the strengths outweigh the weaknesses. 

Overall, the consensus leans positive, with ratings ranging from borderline accept to accept, leading to an overall decision of acceptance (Poster).
Decision: Accept (poster)