Bridging Piano Transcription and Rendering via Disentangled Score Content and Style
Authors: Wei Zeng
JUNCHUAN ZHAO
Ye Wang
Venue: ICLR 2026 Poster (2026)
Keywords: piano transcription
expressive performance rendering
disentangled representation learning
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose a unified framework for piano transcription and rendering.

Abstract: Expressive performance rendering (EPR) and automatic piano transcription (APT) are fundamental yet inverse tasks in music information retrieval: EPR generates expressive performances from symbolic scores, while APT recovers scores from performances. Despite their dual nature, prior work has addressed them independently. In this paper, we propose a unified framework that jointly models EPR and APT by disentangling note-level score content and global performance style representations from both paired and unpaired data. Our framework is built on a transformer-based sequence-to-sequence (Seq2Seq) architecture and is trained using only sequence-aligned data, without requiring fine-grained note-level alignment. To automate the rendering process while ensuring stylistic compatibility with the score, we introduce an independent diffusion-based performance style recommendation (PSR) module that generates style embeddings directly from score content. This modular component supports both style transfer and flexible rendering across a range of expressive styles. Experimental results from both objective and subjective evaluations demonstrate that our framework achieves competitive performance on EPR and APT tasks, while enabling effective content–style disentanglement, reliable style transfer, and stylistically appropriate rendering. Demos are available at https://wei-zeng98.github.io/joint-apt-epr/.

Published as a conference paper at ICLR 2026
BRIDGING PIANO TRANSCRIPTION AND RENDERING VIA DISENTANGLED SCORE CONTENT AND STYLE
Wei Zeng Junchuan Zhao², Ye Wang
Integrative Sciences and Engineering Programme, NUS Graduate School
School of Computing
National University of Singapore
{w. zeng, junchuan @u nus edu, dcswangyênus.c edu. sg
ABSTRACT
Expressive performance rendering (EPR) and automatic piano transcription (APT) are fundamental yet inverse tasks in music information retrieval: EPR generates expressive performances from symbolic scores, while APT recovers scores from performances. Despite their dual nature, prior work has addressed them indepen- dently. In this paper, we propose a unified framework that jointly models EPR and APT by disentangling note-level score content and global performance style representations from both paired and unpaired data. Our framework is built on a transformer- based peuuence-to-sqqueee ing only sequence-alignee data, without requiring fine-grained nevel alignment. To automate the rendering process while ensuring stylistic compatibility with the score, introduce independent diffusion-basee performance style recommen- dation (PSR) module that generates style embeddings directly from score content. This modular component supports both style transfer and flexible rendering across a range of expressive styles. Experimental results from both objective and subjective evaluations demonstrate that our framework achieves competitive performance on EPR and APT tasks, while enabling effective content-style disentanglement, reliable style transfer, and stylistically appropriate rendering. Demos are available at htthhtt::/// //vp:: zeng 98. g thub. o
(Seq2Seq) architecture and is trained us-
INTRODUCTION
Music exists across multiple modalities, notably symbolic music scores and expressive audio record- ings. Converting between these musical modalities is essential for artistic performance and musical analysis, supporting a wide range of applications from artistic creation to music education (Cancino Chacón et al. [223]] CCacón et al.) 2023). In live concert, for example, pianist renders written score into an expressive performance, adding personalized nuances in timing, dynamics, and articula- tion. Conversely, for purposes such analysis, re-performance, archiving. transcription needed to convert an audio recording of performance back into a symbolic representation. These two processes correspond two core tasks music information retrieval (MIR): expressive performance rendering (EPR), which generates performance MIDI (MIDI that captures expressive timing, dynam- ies, and articulation) from symbolic scores (Chacón et al., 2018). and automatic piano transcription (APT), which predicts symbolic scores from performance MIDI (Desain & Honing 1989).
Prior work has studied EPR and APT as two separate tasks (Maezawa et al. 2019
Jeong et al. 2019
Rhyu et al. 2022: Borovik & Viro, 2023 Liu et al.) 2022 Cogliati et al. 2016 Nakamura et al. 2018 20i8|SShibaa et al. (2021). However, as illustrated in the top-left corner of Higure the two tasks are inherently connected, representing inverse transformations between symbolic and expressive forms. In rendering, the performance reflects both the composer's s intent and the pianist's interpretive style; in transcription, the system should filter out these expressive elements recover the underlying score.
Joint modeling in speech tasks such as automatic speech recognition (ASR) and soxt-to-speech (TTS) has shown mutual benefits and enabled weakly supervised training (Ren et al. 2019: Peyser et al. 2022a). concurrent line work in music demonstrates a similar direction, showing that unified translation across multiple modalities be achieved using only sequence-aligned data (Jung et al.

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Published as a conference paper at ICLR 2026
2025), underscoring the growing trend toward scalable, alignment-frees supervision. Motivated by these developments, we propose unified transformer- based framework that jointly learns EPR and APT by modeling two factors: (a) note-level score content representation, which captures symbolic structures like pitch and rhythm; and (b) a global performance style representation, which encapsulates the high-level artistic character of performance (e.g., "heavy" "relaxing") and serves as a conditioning signal to guide the generation of fine-grained expressive details by the decoder. This disentangled representation allows for information sharing across tasks while preserving the interpretability and controllability of the rendering process. Furthermore, the use of unified Seq2Seq architecture enables our model be trained using only sequence-aligned data, removing the need for note-level alignment required by most EPR systems (Rhyu et al.) 2022 Borovik & Viro 2023 Tang
et al. 2023 Jeong et al. 2019: Zhang et al. 2024).
To enable flexible and realistic performance rendering, it is crucial to distinguish between the types of information encoded in our disentangled representations. We define style as the expressive realization of score (e.g.. the "Horowitz factor" by Widmer et al. (2003). and genre the underlying structural and harmonic characteristics of the composition. While both are global attributes, they capture distinct musical aspects. Inspired by recent advances in sheet music classification (Ji et al. 202 Pasquale et al. 2020), we hypothesize that for a performance rendition to sound natural, the chosen style should ideally align with the underlying genre. This suggests that stylistically appropriate performances can be inferred directly from score content, similar to how skilled pianists interpret compositions. Moreover, existing EPR models often rely on composer labels (Jeong et al. 2019 Tang et al. 2023) or require manual control over expressive parameters (Borovik & Viro| [2023 Rhyu al.) 2022), which limits accessibility for non-expert users. Motivated by these observations, we propose a Performance Style Recommendation (PSR) module that generates diverse style embeddings conditioned solely on the score.
We evaluate our framework using both objective and subjective metrics. On standard benchmarks, our joint model achieves competitive performance for both EPR and APT. Subjective evaluations confirm the naturalness of EPR EPR-generated performances. Disentanglement is verified through style transfer and latent space visualizations. In addition, we show that the learned style embeddings encode information about both performer and composer, with composer traits being more dominant. Finally, evaluations of the PSR module demonstrate its ability to generate stylistically appropriate embeddings from content alone.
In summary, this paper makes the following three contributions:
A unified transformer-ban
model for joint EPR and APT, which disentangles score content and performance style representations, and leverages the duality between the two tasks for mutual supervision. This joint formulation enables bidirectional modeling between symbolic and expressive forms of music.
A diffusion-based performance style recommendation (PSR) module, which generates diverse and appropriate style embeddings directly from score content. This module mimics a pianist's ability infer suitable expressive styles from written score, enabling controllable rendering and making style-aware performance accessible to non-experts.
A Seq2Seq formulation of EPR without note-level alignment, which eliminates the need for finely aligned training data and enables scalable learning using only sequence-level supervision. Despite this relaxed supervision, our model achieves competitive performance compared to aligament-dependdet baselines.
2 RELATED WORK
2.1 EXPRESSIVE PIANO PERFORMANCE RENDERING
Early work on EPR relied on rule-based systems (Widmer & Goebl 2004 Chacón et al. 2018
Kirke & Miranda 2013). 3). Recent methods leverage deep learning, including RNN- and LSTM-based
models (Maezawa et al. 2019 J Jeong et al., 2019). as well as transformer- based architectures (Rhyu
et al. 2022 Borovik & Viro 2023 Renault et al. 2023 Tang et al.) 2023). A central challenge in EPR i8 generating performance styles that approprately reflect the content music scores. Existing
approaches often require explicit composer or performer labels (Teong et al. 2019) Tang et al. 2023
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or depend on manual control of expressive parameters (Borovik & Viro 2023 Rhyu al. 2022).
limiting usability for non-expert users. A diffusion-baste model has been introduced to generate expressive control directly from the score, relying hand-crafted note-level style features (Zhang 2024). However, such note-level approach demands intricate, fine-grained adjustments and offers limited flexibility for style transfer between compositions with disparate musical structures.
et al.
Another key limitation of current models (Rhyu et al. 2022 Borovik & Viro 2023 Tang et al. 2023
Jeong et al. 2019 Zhang et al. 2024) is their dependence on note-aligned datasets, which typically require preprocessing with alignment tools (Nakamura et al. 2017). This reliance impedes flexibility, particularly for expressive techniques like trills and mordents that introduce temporal ambiguity. An unsupervised GAN-base approach has been proposed to bypass alignment (Renault et al. (202)) but it is less performant than supervised counterparts. Recent work also explores sequence-aligner supervision scalable alternative: for instance, Jung et al. (2025) demonstrate that unified cross- modal music translation can be effectively learned without strict note-level alignment. Motivated by these developments, we address these limitations by formulating EPR as a Seq2Seq task and introducing a PSR module for automatic style generation.
2.2
AUTOMATIC PIANO TRANSCRIPTION
Automatic piano transcription (APT) methods can be categorized by their input and output modalities.
Input formats include raw audio signals (e.g., waveforms or spectrograms) and symbolic representa-
tions such as MIDI. Output targets are typically note-level sequences (Hawthorne et al. [018 Kim &
Bello 2019 Kong ct al., 2021 Toyama et al. [202] Hawthhrne et al. 2021) or notation-
formats
resembling human readablee sheet music (Román et al. 2019: Alfaoo-Coooooottttt
et al. 20244 Román
ct al. 2018: Zeng et al. 2024; Hiramatsu et al. 2021 Liu ct al. 2021 2022
Shibata et al. [202]
Beyer & Dai 2024). This work focuses symbolic-to-oee sy-bymmolic transcription, where the model
maps expressive performance MIDI to corresponding score sheet representations.
Early APT approaches relied signal processing heuristics (Raphael 2001) and probabilistic
models such as Hidden Markov Models (HMMs) (Cogliati et al. 2016 Shibata et al. 202T). Recent
advances leverage deep neural networks (Liu et al. 2022 Beyer & Dai 2024 Suzuki [021). which
have demonstrated substantial improvements in accuracy and generalization. Particularly, (Beyer
& Dail 2024) proposed Seq2Seq framework that eliminates the need for note-aligned supervision while achieving state-of-the-arr performance. Building on this insight, we adopt similar Seq2Seq framework model score content features within our unified system.
2.3 DISENTANGLED REPRESENTATION LEARNING
Disentangled representation learning (DRL) aims to learn representations that separate the underlying
factors of variation in observed data (Wang et al. 2024). It has been widely studied in computer Chen
vision (Dupont 2018 Yang et al. 2021; Chen et al. 2016 Karras et al. 2020) and natural language processing (He et al. 2017] Bao et al.) 2019; Cheng et al. 2020 Wu al. 20200,, where separating
content from style or semantics has led to improved generalization and controllability.
In music information retrieval (MIR), DRL has recently been explored for disentangling musical content and style to support generation and manipulation (Tan & Herremans] 2020 Wang et al., 2020 Yang et al. 2019 Zhao et al., 2024). One closely related study (Zhang & Dixon 2023) learns content and style representations from expressive performances in unsupervised manner, enabling music analysis and style transfer. In contrast, our work focuses on generating expressive performances from symbolic scores, less-explored but important direction for DRL-based music modeling.
METHODOLOGY
3.1 DATA REPRESENTATION FOR INPUT AND OUTPUT
Input features Following Peyser et al. 2022b), we represent both score and performance inputs as note-level sequences of approximately equal length, enabling the joint encoder to learn domain- agnostic representation of score content. We denote the score and performance sequences as x and y, respectively. Each sequence contains N notes by the order of onset time and pitch, with each note
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Predicted Score Notes
Predicted Perforaace Notes
Style
Score Decoder
Performance Decoder
Dacoddrry
Transcription
Mote
Randaring
Shoet music
Performance
joii sudteako
Style
Disestangled repeosinitionaa
Reconsttucion
Score Encoder
Performance
Style
Purfomanc oo Reconstracion
Enibad
Perforannce
EPR
Soom Notles
Parformano Noleo
APT
PSR
Performance Recommenddato Model
Eroodiea
PSR
Porformance dbyle recommondation model
Figure I: Relationship between EPR and APT (top left) and an overview of the proposed framework. The model comprises a joint transformer- based architecture for EPR and APT, along with diffusion- based performance style recommendation (PSR) module. Four tasks are trained jointly: masked score reconstruction, masked performance reconstruction, expressive performance rendering (EPR), and automatic piano transcription (APT). Score content features 2y and Zy, extracted from score and performance inputs respectively, are encouraged to align. A global style feature z, is learned disentangled factor to support style transfer. The PSR module is independently trained to generate Z, from score content alone, emulating pianist's s ability select appropriate performance styles.
represented as tuple of k; (i {u, y)) discrete symbolic attributes, detailed in Appendix A.2 For score inputs, each note comprises h2 8 attributes, while performance inputs contain ky = 4. The final note embedding is obtained by summing the embeddings of its constituent attributes, resulting Eg, Ey E R where D denotes the embedding dimension.
Output features For score prediction (x), we adopt the representation scheme introduced in Beyer Dai (2024). For performance prediction (ý), we initially applied the same tokenization as used in the input representation, but observed that it degraded generation quality. Since Seq2Seq model does not require note-level alignment, we instead adopt the structured performance representation proposed in Huang & Yang 2020), implemented via the MidiTok library (Fradet et al. 2021).
3.2 UNIFIED MODELING OF EPR AND APT
We consider two domains of symbolic musical sequences: score sequences x E and performance sequences y V. These domains are connected by two inverse processes: expressive perfor- mance rendering (EPR), mapping scores to performances (X' + )), and automatic piano transcription (APT), mapping performances scores ()) + X). Both domains share a latent content space Ze, capturing note-level attributes such as pitch and rhythm. Formally, we treat EPR as one-to-many mapping: addition to content, Y' depends on style space Z,, serving as conditioning signal for the overall control of its performance style. Conversely, APT is formulated as a many-to-one task, where the system aims to filter out cerformance-speccino expressive nuances to recover the unique score content represented in Z Our framework supports training both paired and unpaired data.
Paired setting Given paired data (x, we define content encoders fe,x X + Z, and Say y Ze, along with style encoder fa.y
Z., producing latent representations:
Z = (x) E NDD
Zu fa. E RR NXD,
fx.((y) E R.
(1)
We perform the EPR and APT tasks by decoding from these latent representations: EPR: ý == gy (z. @ 2,), APT: & 9x 9xyz)
(2)
where + denotes broadcasted addition of the global style vector to each time step z.. Both decoders are optimized via cross-entropy losses: CEPR CE(5,y),
CAPT CE(&,x).
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Unpaired setting To incorporate unpaired data, we adopt a masked reconstruction objective inspired by masked autoencoders (He et al., 2022). Specifically, we define x = MASK (x and y = (ys where MASK(-) randomly replaces subset of input tokens with special (MASK) token during encoding. The model is then trained to reconstruct the full original sequence:
Leeex CE(gx (fe,x()))))))))))))))))))))))))) x),
Lee.y CElgy(fes @ fay(y)).y).
3.3 LATENT DISENTANGLEMEN AND REGULARIZATION
We encourage disentanglement between the content space Z, and the style space Z, through both training objectives and architectural design. From a training perspective, The content encoders x and (-) are supervised to capture score-relevant information via losses from APT, EPR, and masked reconstruction tasks. Architecturally, We represent content and style at distinct levels: ze encodes fine-grained, note-level attributes such pitch and rhythm as a sequence of latent vectors, while 22 summarizes the overall expressive style as a single latent vector.
To regularize the style space and promote smoothness, we impose Kullback- divergence penalty between the posterior over 2, and a standard Gaussian prior:
LK.. DxL (g yy) ||,N I)).
The total training objective integrates three components: supervised losses from EPR and APT on paired data, reconstruction losses from masked inputs on unpaired data, and KI. regularization on the style representation:
Liotal
LEPR + LAPT + Lase, + frse.
CKL
paired loss
unpaired loss
regularization
3.4 MODELING OF PERFORMANCE STYLE RECOMMENDATION
After training the joint model with disentangled representations, we introduce independent performance style recommendation (PSR) module that generates style embeddings conditioned solely on score content. This setup mimics the behavior of a pianist who selects expressive style based on the music score alone. The goal is to model the distribution of plausible performance styles for a given score x, enabling flexible and automated expressive rendering.
Training Given a paired sample (x, y). the ground-truth style embedding z, = [y) is extracted from our frozen, pre-trained joint model. A separate score encoder fg.x (.) concurrently extracts global content representation Cg = fg.xx (x). . We then adopt a denoising diffusion probabilistic model (DDPM) (Ho et al. (220)) to learn the conditional distribution p(z, egy) jointly training the diffusion denoiser and g (.) The forward process perturbs the style vector by adding Gaussian noise:
z = V Z + /I ā, E, E~ (O,
and the reverse process learns to denoise z conditioned on eg and the diffusion step l. The style generator gs( (-) is trained to predict the added noise and is optimized using the following objective:
LPSR Ez, st. [| ga (e, z |16]]
(8)
At inference time, given x, a style embedding 2. is generated by sampling from a standard Gaussian prior and iteratively denoising it using the trained model, conditioned on e, (x). The resulting pair (x, ]. is passed to the decoder (-) to synthesize the expressive performance ý.
Inference
3.5 MODEL ARCHITECTURE
Joint model of EPR and APT As illustrated in Figure 1, the joint model consists of five transformer- based components: Score Encoder, Performance Encoder, Style Encoder, Score Decoder, and Perfor- mance Decoder. Each component adopts a standard transformer architecture (Vaswani et al. 2017)
with six layers and eight attention heads, selected for their ability to model long-range dependencies
and scale effectively to large symbolic music datasets. We employ rotary positional encodings (Su let al. 2024), pre-layer normalization (Brown et al. 2020), and SwiGLU activations (Shazeer 2020), with feed-forward hidden dimension of 3072. Decoder outputs are projected to token distributions via parallel linear layers where applicable. To obtain global style embedding, we follow the BERT architecture (Devlin et al. 2019) in the Style Encoder by prepending a special (CLS) token to the input sequence and taking the final hidden state corresponding to this token the style vector.
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Performance style recommendation A separate transformer encoder, architecturally aligned with the Style Encoder, is used to extract a global score representation. A (CLS) token is prepended to the input score sequence, and its final hidden state is used the global content embedding eg. which conditions the style generation process.
During training. a ground-truth style vector 2,, obtained from the joint model, is perturbed using forward diffusion process. The diffusion timestep t is encoded using sinusoidal positional embeddings and concatenated with e, and the noisy style vector z This combined representation passed through a feed-forward network (FFN) to predict the injected noise e. The model is trained using a mean squared error (MSE) loss between the predicted and true noise.
4 EXPERIMENTS
4.1 DATASETS
We use the ASAP dataset (Foscarin et al. 2020) for both paired training and evaluation, as it provides aligned annotations between musical scores and expressive performances. We select 967 high-quality performances and split them into training, validation, and test sets with an 8:1:1 ratio, same as Beyer Dai 2024). To enable unpaired training, we curate an unpaired score dataset consisting of 75,913 public-domain MusicXML files collected from MuseScore We also compile an unpaired performance dataset by soureing piano cover videos from YouTube and transcribing the audio into performance MIDI using state-of-the-ee selected based on a pilot study demonstrating strong accuracy in both note and pedal transcription. To evaluate the generalization of disentangled representations in ouitt--distriuution (OOD) settings, additionally use the ATEPP dataset (Zhang et al., 2022), which contains 11,674 performances by 49 pianists spanning 25 composers, with explicit annotations of both composer and performer identities.
Mudio-to-MII) transcription mode The model is
4.2 TRAINING SETUP
The joint model is trained on NVIDIA A5000 GPUs with total batch size of 36 sequences, each containing 256 notes. Optimization is performed using AdamW (Loshchilov & Hutter (2019) for 40,000 steps, with a cosine decay learning rate schedule and linear warmup over the first 4. 4,000 steps, peaking at 5 x 10-5, Following the regularization strategy proposed by |Beyer & Dai (2024), we apply input dropout (masking) to the decoder sequences to prevent the model from relying solely on local history and to encourage the learning of long-range structural dependencies. Specifically, we use a mask rate of 0.75 for the score decoder in the APT task and 0.2 for the MIDI decoder in the EPR task. The PSR model is trained separately same schedule but with a peak learning rate of x 10
single GPU with batch size of 48, using the
4.3 METRICS
APT We evaluate APT using two widely adopted metrics: MUSTER (Nakamura et al. 2018 Hiramatsu et al., 202T) and ScoreSimilarity (Suzuki 2021 Cogliati & Duan 2017). MUSTER assesses high-level transcription accuracy with a focus on rhythmic structure, including sub-metrics such as pitch edit distance (EE), missing notes (Emiss) extra notes (Eexua), onset deviation (Eonse), and offset deviation (Lotse). ScoreSimilarity also captures pitch-level edit distances (Emiss: Exxur). with additional metrics for stem direction (Estem), pitch spelling (Espet). and staff assignment (Eaat)).
We use both objective and subjective evaluations. Objectively, we compare the generated performance to its human reference and compute three metrics: alignment rate, insertion rate, and missing rate. Additionally, we conduct objective statistics using three metrics (Tang et al. 2023 Zhang et al.] (0024): per-note variance of onset, duration, and velocity; KL divergence from human distributions: and note-aligned mean absolute error (MAE) relative to human references. Subjectively, we conduct listening test with eleven participants trained in music performance. We randomly sample five pieces from Bach, Rachmaninoff, Schubert, Scriabin, and Ravel to cover range of
EPR
htttos:/musescoore..
https://githhb: com/ http://hhhbbbbommmmmmmmerraaaae/

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Published as a conference paper at ICLR 2026
Table 1: APT results on the ASAP dataset. Lower values indicate better performance across all metrics. The best results are shown in bold, and the second-best are underlined. Statistical significance with respect to the end-to-end baseline denoted by i for p < 0.05 and & for p < 0.01.
Method
MUSTER
ScoreSimilarity
Ep
Enie
Exara
Eoen
Extiut
Exr
Emio:
Egem
Ear
Exst
Earn
Equi
Neural Liu et
2022
2.02
6.81
9.01
68.28
54.11
28.04
17.10
17.67
66.98
6,86
9.71
MuseScore MuseScure
2002
2.41
7.35
9.64
47.90
49.44
23.35
16.17
16.74
55.23
21.87
29.87
9.69
Finale MakeMusie,
1988
2.47
10.10
13.46
31.85
45.34
20.64
14.72
16.43
53.35
21.79
26.74
15.34
Shibata et al, 2
(J-Pop
2,09
6.38
8.67
25.02
29.21
14.27
10,80
11.39
71.38
(Classical)
2.11
6.47
8.75
22.58
29.84
13.95
10.74
11.28
64.73
EIM-to-ond Beyer & Dai 2024
2.73
8.40
8.95
17.48
32.92
14.10
12.89
11.29
55.04
11.32
30.51
14.31
Ours
3.08
8.43
7,33
16.26
27.30*
12.48
13.43
9.48
31.75
9.43*
28.60
6.24
Table 2: Objective evaluation of EPR results. We compare variance (o²), KL divergence, and MAE for onsets (O), durations (D), and velocities (V). For x², values closer to the Human reference are better. For all other metrics, lower is better. Best results are in bold; second-best are underlined.
Different letters within column indicate statistically significant differences (p < 0.01).
Method
a² (O)
a² (D)
o² (V)
KI.
MAE (D)
KL.
MAH (V)
Human
0.12
1,72
241.04
Score
0.07
0,07
1.36
13.01
0.46
13.00
29,14"
DExter Zhane et 2024
0.20*
4.15"
238.86"
1.48
0.88
2.32
24.27
VirtuosoNetJeong al. 2019
0.02
0.03
52.54
5.72
0.48
4.91
14.40%
EPRROnll
0.03
0.67
126.04
6.43
0.42
2.05
10.65
Ours (Target) Ours (PSR)
0.02
0.58.
151.03
5.51
0.37
1.76
10,33d
0.02
0.33
161.51
6.19c
0.44
2.67
15.24
Table 3: Objective evaluation of EPR accuracy on test samples using alignment (Align), insertion (Insert), and missing (Miss) rates (p < 0.01).
Table 4: Performer (Perf) and composer (Comp) identification results based on performance style (Style) and score content (Cont).
Method
Align
Insert 1
Miss
Setting
F1
Recall
Precision
Acc.
Score
93.52*
3.57a
2.91a
Style-PPrt 25.82
25.67
27.80
42.07
DExter Zhang et al. 2024
91.27*
5,11
3.62
Cont->Per
0.74
2.02
0.46
9.94
Virtuoso Net Deong ot al. 2019
91 81.88
4.23"
3.90F
Ours (Target) Ours (PSR)
91.55d
4.13
4,32d
Style->Comy $2.45
50.29
55.99
77.46
92.27
3.77
3,96"
Cont->Comp
3.03
4.66
3.75
29.99
genres and styles. Each participant rates the outputs in randomized order on a 5-point Likert scale (I-5) across four dimensions: dynamics, tempo, style, and overall human-likeneds
RESULTS
5.1
EPR AND APT PERFORMANCE
present the APT performance of our model and baseline systems, with statistical significance evaluated using the Wilcoxon signed-rank test (Wilcoxon) 1945). Our model achieves performance comparable the state-of-the-art APT system, indicating that the learned score representations capture key musical attributes such as pitch, rhythm, and structure. Our alignment-frr Seq2Seq formulation achieves competitive results without requiring explicit note-level alignment. In contrast, methods such as Liu et al. (2022) and (Shibata et al. (202)) attain lower pitch errors by relying on note-aligned data, which simplifies pitch and onset prediction, but limits flexibility in musically complex, one-to-many contexts (e.g., ornaments, trills, or expressive deviations).
APT In Table
We compare against two strong alignment- based baselines: Neetu et al. (2019) and DExter Zhang et al. (2024). Our method is evaluated under two conditions: with extracted target and with PSR-generater styles (Ours-PSR). To specifically examine how our joint framework influences EPR performance, we introduce an EPR-Only variant that retains only the Score Encoder, Style Encoder, and Performance Decoder (Section 3.5 and is trained solely on the ASAP dataset. We also take score MIDI (Score) as a baseline model: it is shaded in gray in Table 2 Table to indicate that it is not an EPR model and serves only as comparison anchor. Statistical
EPR
styles
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Score
DExter
(Target Style)
Score
(Tunge Style)
Human
VitunsnN
Ours (PSR)
Humsn
VitumeaNet
Oun (PSR)
Dynamics
Tempo
Style
Overall
Bach
Rachmaninotf
Schshert
Scrishin
Rovel
(a) Subjective ratings of PSR outputs across musical (b) Breakdown of the overall subjective ratings by com- atrributes (dynamics, tempo, and style).
posers.
Figure 2: Subjective evaluation of expressive piano rendering performance across different systems, including human renditions, direct-from-scort baselines, and proposed models.
inddatic Chesn
Vadomi Autherayy
Jobare Semustar
Whltin camplt
Wellpang
Boremook
Screet Rachoorieet
Wadmmi rerovtz
(a) Two-dimensional projection of style embed- dings, colored by composer clusters.
(b) Two-dimensional projection of style embed-
dings, colored by performer clusters.
Figure 3: Two-dimensional visualization of performance style representations from real performances, with colors indicating clusters by composer or performer.
significance is computed by Wilcoxon signed rank test (Wilcoxon 1945) between our methods and all baselines, with < 0.01.
The objective statistics in Table show that models exhibit duration and velocity variances that more closely match those of human performances compared with other baselines, reflecting more natural expressive variability. While DExter shows even larger duration and velocity variance, this does not translate to better quality, as listening tests suggest it results from unstable dynamics rather than meaningful expressiveness. In contrast, our models achieve lower KL and MÁE scores than most baselines (especially Ours-Tarrget,, confirming that they faithfully replicate the fine-grained expressive details found in human renditions. Moreover, the consistent improvement Ours-Target over the EPR-Only variant indicates that joint modeling, together with training on additional unpaired data, leads to better EPR performance, validating the effectiveness of our joint framework.
The accuracy evaluation in Table 3 shows that Ours (PSR) achieves the highest alignment rate (92.27%) and the lowest insertion rate (3.77%), demonstrating the effectiveness of our alignment-free sequence-tos teaeecett--equene formulation. Subjective results in Figure show that Ours (Target) achieves the highest ratings across all attributes and styles, with Ours (PSR) closely following and outperforming baseline systems. Both variants perform strongly across composers, particularly on Bach and Scriabin.
5.2 REPRESENTATION DISENTANGLEMENT
Performer/composeer identification To further analyze the structure of the learned representations, we perform performer and composer identification using score content and performance style representations on the ATEPP dataset (Zhang et al. 2022). which split into training, validation, and test sets with 8:1:1 ratio. We evaluate four model configurations: using either the content performance style representation as input, and predicting either the composer or performer as the target. Each performance MIDI is segmented into 256-note chunks and processed by the trained
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Bareus Romandx Modem
30
-1.
0.3
(a) Two-dimensional projection of style embed- dings extracted from actual performances using the joint model.
(b) Two-dimensional projection style embed- dings generated by the PSR model from corre- sponding scores.
Figure 4: vro-dimensional visualization of style representations across historical eras. Colored regions denote era-specific clusters with centroids marked by black crosses; white arrows indicate temporal progression of musical styles.
joint model to extract latent representations, which are then averaged across chunks to obtain single representation per piece. For visualization, we insert a 2D bottleneck layer before the classification head and project the resulting embeddings onto 2D plane. The classification results and visualization are presented in Table and Figure respectively.
The results in Table 4 demonstrate the effectiveness of the disentangled representations. Classifiers using the style representation 2, achieve substantially higher composer and performer accuracy than those using the content representation Ze. confirming successful disentanglement of performance style from score content. While Ze primarily encodes pitch and rhythmic structure, it is expected to preserve performance-indeeenden musical characters (e.g., composer-specifie information). This explains why the composer classifier using 2g (Cont~>Comp) still achieves a non-trivial accuracy of 29, 99%. Notably, the composer classifier using z, (Style->Comi shows much higher accuracy (77 (6.)..). Beyond the effective disentanglement, we attribute this result to two other factors: first, as a global embedding, z, better suited for capturing high-level stylistic features than the note-level Ze; second, professional pianists often align their performance style with the composer's stylistic conventions, thereby encoding composer information directly into their expression.
The visualization in Figure 3 further supports our findings, with style embeddings forming clear clusters by composer and performer. We also observe that embeddings from human performances contain information about both the artist and the composition. This further supports our assumption that skilled pianists adapt their style to the piece, validating the motivation behind our PSR module.
Style transfer evaluation To further evaluate the disentanglement of content and style, we con- ducted subjective listening test style transfer between pieces from distinct genres. For each test case, listeners rated generated outputs on two criteria: style similarity to a reference performance and overall listening quality. We compared three conditions for the rendered style: the original (Original), the transferred reference style (Target), and an interpolation of both (Mean) to study the learned style feature space. As shown in Figure the Target condition achieves the highest style similarity ratings in Samples and 3, indicating successful transfer. Notably, this improvement does not compromise overall quality. The Mean condition yields consistently strong quality across all samples, suggesting that the style space is well-structured and supports smooth interpolation.
5.3 EFFECTIVENESS OF PSR
To evaluate the styles generated by the PSR model, we collect 5,003 performances from the ATEPP dataset with aligned scores. For each performance, we obtain two style vectors: one extracted directly from the performance using the joint model, and one generated from the corresponding score using the PSR model. Based the title and composer metadata parsed by GPT-4o mini (Achiam et al. 2023), each piece is assigned to one of four historical eras including Baroque, Classical, Romantic, and Modern.

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Sample
Sample
Sample
Similarity
4-
3-
Original
Mcan
(1)
Original (2)
Mean
Target
Original (3)
Mcan
Target (3)
Figure 5: Subjective ratings for three generated samples using different style settings. Listeners rated each output on style similarity and overall listening quality.
We project the style vectors into 2D using the classifier from Section 5.2 As shown in Figure 4 the PSR-generated styles (right) closely mirror those extracted from real performances (left), exhibiting similar clustering structure, era-wise separation, and centroid locations. This alignment, together with the subjective results in Figure 2] supports the PSR model's ability synthesize stylistically meaningful embeddings from score content alone.
CONCLUSION
In this paper, we present unified framework for expressive piano performance rendering (EPR) and automatic piano transcription (APT), built upon disentangled latent representations of score content and performance style. To enable flexible style-aware rendering, we introduce DDPM-based Performance Style Recommendation (PSR) module that generates expressive styles directly from score content. Evaluated through objective metrics, subjective listening tests, and representation visualizations, our approach achieves performance on par with state-of-the-an art methods across both EPR and APT tasks. Our findings demonstrate that: (a) the joint model effectively learns disentangled representations of content and style; (b) EPR can be formulated as a gruuence-to-sequene task without requiring note-level alignment; (c) the model supports flexible style transfer; and (d) the PSR module produces stylistically appropriate outputs conditioned solely on the score. As future work, aim extend this framework popular music, which presents greater stylistic diversity and practical relevance than classical music.
ETHICS STATEMENT
The authors have reviewed and conformed in every respect with the ICLR Code of Ethics https //iclr. cctppublic/CCoeeeeEttaaa..
The human study our experiment is based on online crowdsourcing, which bears minimum risk. Participants are informed that participation in our study enstirely voluntary and that they may choose to stop participating at any time without any negative consequences. No personally identifying information is collected in the human study.
REPRODUCIBILITY STATEMENT
We introduce our dataset and experimental settings in Section 4. and ection respectively. We also provide details model architectures necessary for reproduction Appendix be released upon acceptance with sufficient instructions for reproducing the model architecture and training pipeline using public datasets such as ASAP and ATEPP.
B The code will
ACKNOWLEDGMENTS
We would like to thank Qihao Liang for his valuable advice on writing. We also appreciate Xichu Ma for his assistance during the rebuttal process.
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APPENDICES
The appendix is structured into main parts. Appendix(A|speeifies the data processing details involved in the paper. Appendix B presents implementation details of our proposed methods. Appendix C [ presents supplementary experimental results on GPT-4o mini output verification, diversity analysis of EPR, and ablation studies. Appendix D discusses challenges and future work. In Appendix [F) we provide several examples of expressive piano rendering (EPR) and automatic piano transcription (APT). Finally, we disclose the use of LLMs in Appendix
provides subjective listening test details. Appendix
A
DATA PROCESSING DETAILS
A.1 1 DATA FILTERING
To construct clean and consistent symbolic dataset from MuseScore, we apply series of rule-based filters to exclude low-quality incompatible piano scores. A score is retained only if it satisfies all of the following criteria:
Staff structure: The score must contain exactly two staves, conforming to standard piano notation.
Note count: The total number of notes must be at least 100.
Bar count: The score must span at least 10 bars.
Note density:
No individual bar may contain more than 64 notes, to avoid overly dense
notation.
Time signature: The time signature must fall within musically plausible range: the number of beats per measure must be between 1 and 16, and the beat type must belong to the set {2, 4, 8, 16, 32).
Key signature: The notated key signature, expressed as the number of fifths, must lie within 7, 7) In addition, the mean distance between the notated and estimated keys (Temperley 1999
Cancino-Chacee et al., 2022 must not exceed 1.
To compute key signature distance, we segment each score into contiguous regions with a constant notated key signature. For each segment, we estimate the key and compare it to the notated key. Let k E [- denote the notated key signature and kę E [[7, 7] the estimated key. The key distance is defined as:
dj min (k:
kul, 1k: - k 12), |k; - hs 1122)
(9)
accounting for circularity in the circle of fifths. The final mean key distance is computed as:
N
D
de
(10)
where is the number of key-stable segments. Only scores with < are retained.
A.2 DATA REPRESENTATION DETAILS
Score The score representation captures structural and timing information relevant for expressive rendering. The input encodes performance-related features, while the output is extended to include additional specifc attributes necessary for producing readable sheet music.
Time-based features, including inter-onset interval (IOI), onset-in-bar, note value, and downbeat, are discretized into consistent vocabularies spanning 0 to 6 quarter lengths, each with 145-146 bins. Boolean-valle valued attributes, such as grace note and hand/staff assignment, are encoded as binary values. The score output additionally predicts symbolic formatting elements such as voice number, articulation markings (c.g., trill, staccato), and engraving segraving-specifiee cues including stem direction and accidentals (c.g., double flats and sharps). All features are treated as discrete classification targets using small, well-defined vocabularies summarized in Table 5
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Table Vocabulary size and value ranges of imput and output parameters for music score.
Parameter
Nyocab
Range/Values
Input
Output
Onset-in-Bar
145
[0. 6] quarter-length
Inter-Onset Interval (IOI)
145
[0. 6] quarter-length
Pitch
128
[0. 127]
Note Value
97
[0. 4] quarter-length
Mcasure Length
146
[0, 6] U {false]
Hand/Staff
2
boolean
Trill
2
boolean
Grace
2
boolean
Staccato
2
boolean
Voice
8
[1. 8]
Stem
3
(up, down, none)
Accidental
6
{bb, 7, #. #t. none)
Table 6: Vocabulary size and value ranges of input and output parameters for performance MIDI.
Parameter
Nyocub
Range/ Values
Input
Output
Pitch (p:)
128
[0. 127]
IOI (o:)
200
[0. 8] seconds
Duration (d.)
200
8] seconds
Velocity (8))
128
[0, 127]
Pitch Token
[21, 108]
Duration Token
discrete steps
Velocity Token
32
32 velocity bins
Time-Shift Token
65
quantized by beat.res
Special Tokens
2
{PAD, BOS}
Performance MIDI The performance representation captures expressive aspects of human exe- cution, including timing, articulation, and dynamics. At the input level, we extract four note-level features: Pitch (MIDI number), IOI (inter-onset interval in seconds), Duration (extended by pedal usage), and Velocity (loudness). IOI and Duration are quantized into 200 bins, while Velocity is coarsely grouped into 8 bins for robustness.
For output, we adopt a structured token- representation (Huang & Yang 2020). implemented using the midit tok library (Fradet al. 2021). The model generates discrete token sequences that include Note-On, Duration, Velocity, and Time-Shift events, enabling expressive sequence generation without explicit note-level alignment. Special tokens such BOS (beginning of sequence) and PAD are also used to facilitate training and formatting. Table provides the vocabulary sizes and ranges for all input and output features.
B IMPLEMENTATION DETAILS
B. JOINT MODEL
Our joint model is implemented in PyTorch Lightning and trained via multi-task learning to simulta- neously handle EPR. APT. and masked reconstruction from unpaired data. This section outlines the training tasks, loss formulation, optimization strategy, and implementation setup.
Training tasks
Each training step involves four supervised or self-supervised subtasks:
APT The score decoder reconstructs symbolic score tokens from the performance content encoder.
EPR The performance decoder generates MIDI tokens conditioned on the score content encoder and style embedding.
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Score Reconstruction The score encoder is trained using random masking to reconstruct full sequences from partially masked inputs.
MIDI Reconstruction The performance content encoder and decoder reconstruct MIDI sequences from masked inputs similar fashion.
Additionally, Kullback-Leibles (KL) regularization term applied to the style embedding to encourage compactness and diversity in the latent style space.
Training loss
Let CAPT, LEPR, Lree.x' and L denote the cross-entropy losses for APT, EPR, score reconstruction, and MIDI reconstruction, respectively. The total training objective is given by:
Liaal CAPT
LEPR
Are (Cree,x + Leery)) + AKL. LKL.
(11)
where Are = 0.2 and ÅKI. 0.1. We apply a 50% masking rate to encoder inputs during reconstruc- tion, and lighter masking rate of 10-20% to decoder inputs to improve robustness and mitigate overfitting.
Optimization We use AdamW optimizers [Loshchilov & Hutter 2019) with learning rate of 5 x 10-5, following a cosine learning rate schedule with 4,000 warm-up steps and 40,000 total steps. Gradient updates are manually scheduled, and training is performed using mixed precision (fp16).
Batching and scheduling Each training step processes 144 sequences (each of length 256 notes), evenly divided among the four subtask types: APT, EPR, unpaired score, and unpaired MIDI. Data loaders for each subset interleaved and sampled in parallel. KL regularization is computed once per batch using the mean and variance of the predicted style embeddings.
Implementation notes All model components use unified embedding dimension of d = 512, with task-specific embedding layers. Attention masks are dynamically modified during training to simulate incomplete inputs, following masked language modeling strategies. The system is trained on 3 NVIDIA A5000 GPUs using batch-level data parallelism.
B.2 PERFORMANCE STYLE RECOMMENDATION (PSR)
The performance style recommendation (PSR) module is designed to generate expressive style embeddings directly from symbolic scores, enabling performance rendering without requiring paired expressive data at inference time. The overall architecture is illustrated in Figure 6
Overview The PSR model comprises two components: (1) transformer-baat based score encoder that extracts a global content embedding from symbolic score sequence, and (2) denoising diffusion probabilistic model (DDPM) that generates a style vector conditioned on this content embedding. This pipeline enables sampling stylistically coherent vectors from Gaussian noise, guided by the structure of the input score.
Score encoder We adopt transformer encoder fg.x (x) to process the input score sequence. Following the BERT-style design (Devlin et al.] [201)). special [CLS] token prepended the sequence, and its finel-layer hidden state is used as the global score content representation eg RD..
Diffusion network We employ DDPM (Ho et al. 2020) with velocity prediction (Salimans & Ho 2022) to model the conditional distribution over style embeddings given the content vector. During training, the model lears recover ground-truth style vector 2,, extracted from human performances via the joint model, from noisy version z', produced by the forward diffusion process. sinusoidal timestep embedding e, concatenated with the projected content embedding e', and the noisy style vector z and passed through multi-layer perceptron (MLP) predict the velocity target Viarget. The model is optimized with the following mean squared error loss:
LPSR Ex,,, [llaa(z's er, e,,) - Virget
(12)
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Trardfomme Enceder
IOI
Egi
Aeaen
Step Embedding
Hand
Exuuas
eg
ey
Piach
<CLS>
LUSE
Layer Norm
FCN Layer
Lineer
SIUU
XN
Linear
g.(c, Rc. x
Figure 6: Architecture of the performance style recommendation (PSR) module, Given a symbolic score, we extract a global content embedding using transformer encoder and train a diffusion model to predict the style embedding from noise.
Table 7: Comparison of model parameters and inference speed for APT and EPR.
Method
Number of Parameters
APT Inference speed
EPR Inference speed
Dexter (Zhang et al. 2024
62.41 M
14.14 s/sample
Virtuosonet (Joong et al. 2019
5.16 M
0.35 s/sample
End-to-End (Beyer 2024
32,60 M
4.56 s/sample
Ours
188.21 M
4.86 s/sample
10.17 s/sample
Inference At inference time, style vector initialized from standard Gaussian distribution and iteratively denoised using the exponential moving average (EMA) version of the MLP denoising network. The resulting style embedding é, can be combined with the score content condition the expressive rendering model. This one-to-mann mapping enables diverse, plausible, and stylistically appropriate generation from symbolic input alone.
B.3 MODEL COMPLEXITY
Table 7 summarizes the model sizes and inference speeds for both APT and EPR, tested on single NVIDIA A5000 GPU. Several observations can be drawn. First, although our unified model contains substantially more parameters (188.21M) than the end-to-end APT baseline (32.60M), its APT inference speed (4. (4.86//sample) remains comparable because only the APT-specific modules are active during APT decoding: the additional EPR-related parameters are not involved in this forward pass. Second, APT inference is consistently faster than EPR within the unified architecture. This follows from the shorter output sequences in APT, whereas EPR must generate longer sequences under the structured performance representation (Section 3.1. Third, EPR inference speed varies widely across baseline systems. VirtuosoNet is the fastest (0.35s/sample) as it trained on oote-aligned data and does not require autoregressive decoding, while DExter is the slowest (14. 14.114s 14s/sample) due to its diffusion-basel design requiring multiple denoising iterations. Our unified model falls between these two extremes: despite performing autoregressive decoding and modeling fine-grained expressive attributes, it achieves a reasonable EPR inference time (10. 17s/sample) while supporting both tasks within a single architecture.
C SUBJECTIVE LISTENING TEST INSTRUCTIONS
C.1 OVERVIEW
We conduct our subjective evaluation using a Google Form structured into two sections: (I) evaluation of performance style recommendation (PSR), and
evaluation of style transfer. Each
https://doccs.oog/ com. forms
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For toaa of restikee
Oveall Bodlantin
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Fendition
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Rangrn
Ratings Renditdoe
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(a) Overall evaluation of EPR.
(b) Style transfer evaluation.
Figure 7: Screenshots of survey pages and instructions of our online survey.
participant completes both sections, with average completion time of approximately 32 minutes. Figure 7| shows sample survey pages along with participant instructions. Detailed descriptions the survey structure are provided below.
C.2 SURVEY STRUCTURE
Part I: Overall Evaluation Participants are presented with music clips, each accompanied by audio renditions generated by different EPR models. Each rendition is rated along the following four dimensions:
Dynamies: Naturalness and expressiveness of loudness variation.
Tempo: Naturalness and expressiveness of tempo fluctuations over time.
Performance Style: Appropriateness of the performance's character, mood, and interpreta- tion.
Overall Human-Likeness:: How convincingly the performance resembles that of a human.
Ratings are provided 5-point Likert scale ranging from (Very Poor) 5 (Very Good).
Part II: Style Similarity Participants are presented with examples. Each example consists of:
A reference performance.
Three test renditions generated by different models, with varied content but intended to share the same performance style.
Each test rendition rated on:
Performance Style Similarity: The extent to which the style (c.g., rhythm, dynamics, pedal usage) matches the reference, independent of pitch content.
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Table 8: Agreement matrices between human annotators and GPT-4o mini Cohen's values:
Annotator (A1) v.s. Annotator 2 (A2) 0.89; Annotator 1 (A1) v.s. GPT-4o mini = 0.85; Annotator 2 (A2) v.s. GPT-4o mini 0.89. Baroque, C Classical, Romantic, M Modern.
A1 vs. A2
A1 vs. GPT-4o mini
A2 vs. GPT-40o mini
B
M
C
R
M
C
R
M
22
0
22
0
C
0
10
4
0
6
8
0
0
6
4
0
R
0
53
0
55
0
58
M
0
0
0
2
8
Table Average pairwise MAEs for human renditions and model outputs.
Duration MAE
Velocity MAE
Human
0.06
11.62
Model
0.08
8.01
Overall Human-Likenes Perceived expressiveness and realism of the performance.
All ratings are again provided on a 5-point Likert scale.
C.3
ADDITIONAL NOTES
Participants instructed to evaluate variation and human-likenese rather than personal preference or audio fidelity.
All audio sources are amonymized; both the order of clips and model outputs are randomized to reduce potential bias.
Participants encouraged headphones in quiet environment for optimal listening conditions.
The total duration of the survey is approximately 25-30 minutes. No personal data
collected.
D SUPPLEMENTARY EXPERIMENTAL RESULTS
D. HUMAN VERIFICATION OF GPT-40 MINI OUTPUTS
To assess the reliability of GPT-40 mini predictions in Section we conducted human verification study on 100 randomly sampled movements, independently annotated by two professionally trained pianists into four eras (Baroque, Classical, Romantic, Modern). Agreement was measured using Cohen's 's
Bo- Bo-Ps where Po the observed agreement and p. is the expected agreement by chance,
As shown Table 8 inter-annotator agreement was high (x 0.89), and GPT-4o mini showed 0.89). Most disagreements occurred in transitional works between Classical and Romantic eras, where stylistic boundaries are ambiguous. For example, Piano Sonata No. 26 in E-flat, Op. 8la "Les adieux": II. Abwesenheir (Andante espressivo) was annotated as Classical by both human experts but labeled as Romantic by GPT-4o mini. Such cases reasonable given the transitional nature of the repertoire. Overall, these results confirm that GPT-40 mini aligns closely with expert judgment and can be used as a reliable reference for PSR evaluation.
similarly strong consistency with both annotators (K 0.85 and
D.2 DIVERSITY ANALYSIS OF EPR
To verify that the model captures one-to one-to-many expressive variation rather than collapsing to averaged output, we analyzed diversity on score from ASAP with 7 human performances and model outputs generated via top-k sampling (k 5). Pairwise note-aligned MAEs were computed
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Table 10: Pairwise MAES among 7 human renditions.
(a) Durations
(b) Velocities
H1
H2
H3
114
H5
H6
H7
II1
HI
0.00
0.06
0.07
0.06
0.06
0.07
0.06
0.00
10.66
15.82
12.46
12.90
H2
0.00
0.06
0.06
0.05
0.06
0.05
H2
0.00
13.23
13.82
11.01
12.23
12.26
H3
0.00
0.07
0.07
0.06
0.06
H4
0.00
12.16
10.80
11.26
H4
0.00
0.06
0.08
0.05
H5
0.00
11.12
10.78
H5
0.00
0.06
0.05
H6
0,00
9.66
0.00
0.06
H7
0.00
0.00
Table 11: Pairwise MAES among 7 model outputs.
(a) Durations
(b) Velocities
M1
M2
M3
M4
M5
M6
M7
M1
M2
M3
M4
M5
M6
M7
0.00
0.08
0.13
0.06
MI
0.00
6.09
10.06
9.47
7.73
8.26
8.14
M2
0.00
0.08
0.10
0.06
0.09
0.09
M2
0.00
9.82
8.53
8.61
9.94
9.62.
M3
0.00
0.08
0.06
0.05
0.04
M3
0.00
6.17
10.12
7.48
8.45
M4
0.00
0.07
0.09
0.08
8.40
M5
0.00
0.07
0.05
M5
0.00
6.50
5.00
M6
0.00
0.06
M6
0.00
4.37
M7
0.00
M7
0.00
Table 12: APT results on different proportions of paired/unpaired data. Lower is better for all metrics. The best results are shown bold, and the second-best are underlined.
MUSTER
ScoreShmilarity
Methed
Ep
Emiss
Exura
Emss
Exfiet
Eay
Emiss
E
Exu:.
Exat
Extem
Espell
paired + 0% unpaired
3.10
9.33
8.09
16.69
29.29
13.30
13.98
10.13
59.45
10.02
30.60
8.44
paired + 25% unpaired
2.94
8.86
7.80
16.37
28.36
12.87
13.66
10.10
60.06
8.86
30.58
7.46
paired 50% unpaired
3.24
9.74
7.59
17.07
27.99
13.13
14.91
56.86
7.91
31.61
10.49
paired 100% unpaired
3,08
8.43
7.33
16.26
27.30
12.48
13.43
9.48
51.75
9.43
28.60
6.24
Table 13: Performer (Perf) and composer (Comp) identification under two data settings: paired + 0% unpaired and paired + 100% unpaired. Boldface is kept only for Style-Perf and Style-CComp highlight the effect of adding unpaired data. The rightmost block reports the per-metric gain A (100% unpaired - 0% unpaired).
paired + 0% unpaired
paired 100% unpaired
(100%
0S)
Setting
Recall
Precision
Acc.
F1
Recall
Precision
Acc.
AFI
ARec.
APrec.
AAcc.
Style-PPrff
19.33
19.17
20.21
33.76
25.82
25.67
27.80
42.07
46.49
+6.50
47.59
48.31
Cont->Perf
0.71
1.94
0.44
9.68
0.74
2.02
0.46
9.94
+0.03
+0.08
+0.02
+0.26
Style Comp
46.33
43.51
55.24
69.07
52.45
50.29
55.99
77.46
+6.12
+6.78
40.75
+8.39
Comt->Comp
2.92
4.57
4.37
30.16
3.03
4.66
3.75
29.99
+0.11
+0.09
-0.62
-0.17
for durations and velocities. As summarized in Table 9. the average human MAEs were 0.06 (duration) and 11.62 (velocity), while the model achieved 0.08 and 8.01, respectively. Detailed pairwise matrices Table 10 Table II) show that model outputs exhibit meaningful internal variation, following the diversity observed in human renditions. This demonstrates that the proposed model captures distributional expressiveness in performance generation rather than regressing to mean output.
D.3 ABLATION STUDIES
Ablations unpaired data To evaluate the impact of unpaired data, conduct ablation study by varying the ratio of unpaired data used in training. We train four model variants using 0% (paired data only), 25%, 50%, and 100% of our curated unpaired datasets, while keeping all
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Table 14: Ablation of KL weight on KL divergence, active units (AU), and classification accuracy (CA).
KL weight
KL divergence
AU
CA
1.11
0.94
0.5
0.69
0.91
1
0.09
0.88
5
0.10
0.76
Inloii Chapk bleen Saberiic Buch
Viadmar
Clec
staken
Wind Sumplt Deristaaeebdn
Veltgny Amockes Socart
Feature
(a) vro-dimennsonal projection style embed- dings, colored by composer clusters.
(b) Two-dimensional projection of style embed-
dings, colored by performer clusters.
Figure 8: Two-dimensional performance style visualization of paired + 0% unpaired variant repre- sentations, with colors indicating clusters by composer or performer.
other hyperparameters constant. The APT results in Table 12 show that incorporating unpaired data generally enhances performance. Adding just 25% of the unpaired data provides a consistent improvement over the baseline model trained only on paired data, while using the full 100% unpaired dataset achieves the best overall performance.
Furthermore, study the influence of umpaired data representation disentanglemenn, conduct performer and composer identification Section55.2] As shown in Table 13] introducing unpaired data significantly enhances the quality of the style representation. For both performer (Style-PPerl and composer
identification, all metrics see substantial improvement, with classification accuracy increasing by +8.31% % and +8.39% respectively. In contrast, the classification performance using the content representation remains almost unchanged. These results indicate that our model effectively leverages unpaired data enrich the style embedding while successfully preserving the disentanglement between performance style and score content.
To further examine the effect of unpaired data, we visualize the learned style embeddings for the paired + 0% unpaired variant and the paired + 100% umpaired variant (Figure 8). Compared with Figure 3] the paired 0% unpaired variant exhibits noticeably less structure, with composer and performer clusters partially overlapping. Incorporating unpaired data produces clearer and more compact clusters, indicating that the additional data helps the model learn more discriminative and coherent style representations.
KI divergence analysis We evaluate latent informativeness across different KI. weights for the KI. divergence loss introduced in Section 3.3 using three metrics (Wang al.] 2021): (i) KL divergence between posterior and prior, (ii) Active Units (AU) measuring the number latent dimensions with sample variance > 0.01, and (iii) style classification accuracy (CA) using 2, and ground-truth era labels from Section 5.3 As shown in Table 14] stronger KL regularization reduces both KL divergence and classification accuracy, while the number of active units remains consistently high (512). This indicates that although some information compression occurs, the latent representation does not undergo full posterior collapse, and still preserves musically meaningful information.
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Published as a conference paper at ICLR 2026
E DISCUSSION ON CHALLENGES AND FUTURE WORK
Beyond classical genres Our joint framework is inherently genre-agnostic: it does not rely on classical-specific score structures and can, in principle, generalize to any musical style given suitable supervision. Our current study focuses on classical piano performance primarily due to data avail- ability, as existing score- score-perforee porre-peffrrance aligned datasets such as ASAP (Foscarin et al. 2020) contain exclusively classical repertoire. Extending expressive performance rendering to other genres (e.g.. jazz or pop) introduces additional challenges. First, these genres lack large curated paired datasets, making supervised learning more difficult. Second, many non-classical traditions involve improvisa- tion, flexible phrase structures, and rhythmically nuanced conventions (e.g., swing timing) that are not well captured by classical-style fully notated scores. For instance, jazz performances are typically aligned to lead sheets rather than detailed five-line staff notation, making precise score-performance alignment inherently ambiguous. As future work, aim to curate genre-specific datasets and adopt notation formats appropriate to each style (e.g.. lead sheets for jazz, chord charts for pop) to extend our unified EPR framework beyond classical music and enable genre-aware expressive rendering.
Transcription biases Our use of unpaired YouTube performances offers valuable stylistic diversity and follows data construction strategy similar to ATEPP (Zhang et al. 2022). Nonetheless, this pipeline may also introduce transcription-relteed artifacts, as the audio-to-Min
system can impose quantization biases or systematic timing regularities. As a result, the style encoder may inadvertently learn these artifacts rather than capturing purely human expressive behavior. In the long term, we aim to pursue end-to-end performance modeling by generating audio directly from score notation, thereby mitigating domain shifts introduced by intermediate MIDI representations and allowing the model to learn stylistic nuances more faithfully from raw human performances.
F EXAMPLES OF EPR AND APT
EPR Demos are available at https::/wett gieengg..githb.... joint-nntttttttto epr/
The page includes two sections: (1) rendering results from various models, including ours, five music pieces from different composers; and (2) style transfer results on three music pieces, showcasing the flexibility of our method.
For each sample, the ground ground-truth score is displayed at the top, and the predicted score from our model at the bottom. Missing notes in the target scores are highlighted with red bounding boxes, while inserted notes in the predicted scores are highlighted with blue bounding boxes.
APT Examples of APT outputs are shown in Figure Figure 11
G THE USE OF LARGE LANGUAGE MODELS (LLMs)
In accordance with the ICLR policy, we disclose the use Large Language Models (LLMs) as assistive tools in the preparation of this manuscript. The specific applications are detailed below:
Data annotation: We employed an LLM to assist in the annotation of our dataset, The detailed methodology and human verification have been introduced in Section 5.3 and Appendix D.I
Literature search: LLMs were used as a tool to aid in the initial search and summarization of relevant prior work.
Writing and polishing: We utilized an LLM for proofreading and language refinement.
All authors have carefully reviewed and edited the manuscript. We take full responsibility for all content of this paper, including the final research ideas, experimental results, and the accuracy and integrity of the text.
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Figure 9: Ground truth score (upper) and transcribed score (lower) from Piano Sonata No. in Major, K 332, by Wolfgang Amadeus Mozart (APT sample 1).
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Figure 10: Ground truth score (upper) and transcribed score (lower) from Keyboard Sonata in E
major, HVb.3VI by Franz Joseph Haydn (APT sample 2).
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Figure 11: Ground truth score (upper) and transcribed score (lower) from Ballade No. 1 in G minor,
Op. 23, by Frédéric Chopin (APT sample 3).
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Reviews:
[{"review_id": "u0Eaki8mov", "reviewer": "Reviewer_dQyX", "summary": "This paper presents a model for joint transcription and expressive performance rendering for classical piano music. The model is based on a VAE-like architecture with a performance style recommendation module to better sample performance latent code.", "questions": "Sec 3.2: \"Our framework supports training on both paired and unpaired data.\" - again, does it support training only on unpaired data?\n\nIn the demo page, most human performance have sudden note cutoffs that are very unnatural, which I suspect to be some errors in data preprocessing. This might affect the subjective evaluation as well. Could the author double-check the rendering and MIDI preprocessing code?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 4, "contribution": 2, "strengths": "The task of jointly modeling performance and score is very important in computer music, especially in symbolic music understanding and generation. The content-style disentanglement is a reasonable way to jointly model music performance and score, and the methodlogy is in general novel. \n\nThe paper is generally well-written.", "weaknesses": "There are many things unclear about the model's performance:\n\n1. It is unclear whether the joint architecture improves the performance compared to single model. The ablation study in D.3 does not stress this point.\n\n2. It is uncleared whether the disentanglement works on unpaired data. The framework lacks explicit inductive biases (except for KL loss) to perform such disentanglement. All composer and performer in Fig. 3 (assumed also used in Tab. 4) seem to be included in the paired dataset (correct me if I am wrong), so the model's disentanglement ability on unseen performer/composer remains unclear.\n\n3. It is uncleared whether the unpaired data helped or not. As shown in Table 11, the effect of unpaired data on APT performannce is quite limited, and the performance does not always increase when you put in more unpaired data.\n\nFig 8-13 are very hard to read. \n\n1. It would be better to combine them into a single sheet music, with different colors (e.g., red: missing, blue extra) to denote inconsistency between the ground truth and the prediction.\n2. I totally understand that the model does not predict a key signature, but it would still be better to show Fig. 13 under the ground-truth key signature, otherwise it makes little sense to a musician.\n\nLastly, the performance of the system on APT is far from practical use. I understand that performance to score is considered a very hard task in modern MIR, so it is generally hard to improve. Still, I do not think this paper made significant contribution to the task.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xxV3LvrM6f", "reviewer": "Reviewer_zyQd", "summary": "This paper proposes a unified transformer-based framework for jointly modeling expressive performance rendering (EPR) and automatic piano transcription (APT) by disentangling note-level score content and global performance style representations. It leverages paired and unpaired data without requiring note-level alignment, and introduces a diffusion-based performance style recommendation (PSR) module to generate style embeddings from scores alone. The authors claim competitive performance on benchmarks, effective disentanglement for style transfer, and stylistically appropriate rendering. Contributions include the joint modeling approach, the PSR module, and an alignment-free Seq2Seq formulation.", "questions": "1. Could the authors clarify how the model handles performer-specific biases in score-performance alignment? For example, in APT, how do you ensure the output scores are readable and musically coherent (e.g., correct time signatures, proper note grouping)? The examples in the paper seem to have errors in this regard—would incorporating additional regularization or post-processing improve this, and have you experimented with it?\n\n2. For the PSR module, how does it perform on scores from underrepresented genres or composers? Could you provide quantitative evidence (e.g., style appropriateness metrics) on OOD data like ATEPP to show generalization?\n\n3. The paper mentions competitive performance, but some metrics (e.g., onset/offset deviations in APT) lag behind alignment-dependent baselines. What additional experiments (e.g., with more unpaired data or fine-tuning) might close this gap, and could this change the evaluation of the alignment-free approach?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper demonstrates solid experimental validation, achieving competitive results on EPR and APT tasks using the ASAP dataset, with both objective metrics (e.g., MUSTER and ScoreSimilarity for APT, variance/KL/MAE for EPR) and subjective listening tests. \n\nThe disentanglement of content and style is a meaningful addition, enabling style transfer and recommendation, which adds interpretability and controllability to the tasks. The use of unpaired data and masked reconstruction objectives allows scalable training without strict alignment, addressing a practical limitation in prior work. \n\nOverall, this represents an incremental but technically sound advancement in the niche area of symbolic music processing, with clear significance for music information retrieval applications like education and analysis.", "weaknesses": "The novelty is somewhat limited for the broader ICLR community, as the core architecture—a dual-tower encoder-decoder setup with disentangled representations—builds on classic Seq2Seq models like MASS, without introducing fundamentally new insights that could generalize beyond music tasks. \n\nWhile the style disentanglement is emphasized, the paper does not deeply address challenges in score readability; for instance, the transcribed scores exhibit errors in time signatures and poor note readability (e.g., improper grouping or stem directions), which is a critical limitation for practical APT applications, as each performer's bias introduces non-strict alignments that require more sophisticated post-processing. \n\nExperiments are adequate but could be strengthened with more diverse OOD evaluations or comparisons to recent baselines like those in Zhang et al. (2024) for EPR. Additionally, the reusable insights (e.g., for general disentangled learning) are constrained to this small domain, reducing broader impact.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "yMNXV3yzPx", "reviewer": "Reviewer_eiLe", "summary": "This paper presents a unified, transformer-based framework for jointly modeling Expressive Performance Rendering (EPR) and Automatic Piano Transcription (APT), two fundamental and inverse tasks in Music Information Retrieval (MIR). The core contribution lies in the effective disentanglement of note-level score content ($Z_c$) and global performance style ($Z_s$) representations. This is achieved through a multi-task, sequence-to-sequence architecture trained on both paired (ASAP) and unpaired (MuseScore, YouTube) data.\n\nA key strength of this framework is its ability to operate on sequence-aligned data, removing the common and cumbersome dependency on fine-grained, note-level alignment. Furthermore, the authors introduce a novel diffusion-based Performance Style Recommendation (PSR) module, which learns to generate appropriate and diverse style embeddings ($Z_s$) conditioned solely on the score content. This enables high-quality, automated expressive rendering without requiring a reference performance.", "questions": "1. The PSR module uses a global content embedding $e_g$ to generate a single, global style vector $Z_s$. Does this global vector have sufficient capacity to guide performances where the intended style changes significantly between sections (e.g., a piece with a slow, gentle introduction (Adagio) followed by a fast, aggressive main theme (Presto))? Or does the model tend to \"average out\" the style over the entire piece?\n\n2. Regarding the unpaired performance data (transcribed from YouTube): Did the authors observe any \"model-like\" artifacts in the learned style representations, or does the model seem robust to this potential domain shift? For example, does the style space show any clustering based on the source of the data (ASAP vs. YouTube)?\n\n3. Related Work Suggestion: The paper's contribution of leveraging sequence-aligned data over note-level alignment is highly significant. We strongly suggest the authors also discuss and cite the recent work by Jung et al. (2025), \"Unified Cross-modal Translation of Score Images, Symbolic Music, and Performance Audio\" (arXiv:2505.12863v1). This paper shares a similar and ambitious motivation of unified multi-modal translation (extending to score images and audio) and also highlights the importance of facilitating large-scale learning from sequence-aligned pairs, rather than relying on strict note-level alignment. Citing this work would help to better contextualize the paper's contribution within this important and emerging research trend.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The joint modeling of the inverse EPR and APT tasks is an elegant and powerful formulation. By leveraging the duality of these tasks (EPR learns to add style, APT learns to remove it), the model develops a robust and semantically meaningful disentanglement of content and style. This joint approach is a novel contribution to the field of symbolic music modeling.\n\n* The most significant practical contribution is the move away from note-level alignment. The reliance on finely-aligned datasets has been a major bottleneck in performance modeling, as such datasets are small and expensive to create. By demonstrating competitive results using only sequence-level alignment (and further leveraging unpaired data), this work opens the door to training on much larger and more diverse datasets, which is a crucial step forward for the field.\n\n* The methodology is sound and the paper is technically strong.\n  * The architectural design choice (note-level sequence $Z_c$ vs. global single-vector $Z_s$) is a simple and effective inductive bias for disentanglement.\n  * The introduction of the diffusion-based PSR module is a creative and modern approach to solving the problem of \"what style to apply\" when no reference is given.\n  * The experimental validation is thorough. It includes comprehensive objective metrics for both APT (MUSTER, ScoreSimilarity) and EPR (KL, MAE, alignment), as well as crucial subjective listening tests that confirm the quality of the generated performances.\n  * The disentanglement is not just claimed but empirically verified through well-designed identification tasks (Table 4) and latent space visualizations (Fig 3), which clearly show the style vector $Z_s$ captures performer/composer information while $Z_c$ does not.\n\n* The paper is exceptionally well-written and easy to follow. Figure 1 provides an excellent and clear overview of the complete framework and the relationships between the different tasks. The concepts of content/style and the mechanism for disentanglement are explained intuitively.", "weaknesses": "* The framework is evaluated exclusively on classical piano music. While this is a standard benchmark, the authors' claim of style disentanglement would be further strengthened by testing on genres with more starkly different expressive conventions (e.g., jazz, pop). The authors note this as future work, but a brief discussion of the potential challenges (e.g., improvisation, complex swing rhythms) in extending the model would be beneficial.\n\n* The use of unpaired data from YouTube, transcribed via a SOTA audio-to-MIDI model, is a clever strategy for data augmentation. However, this introduces a potential domain bias. The style encoder might be learning artifacts specific to that transcription model rather than \"true\" human performance style. The results seem robust, but a brief acknowledgment or discussion of this potential methodological caveat would be appropriate.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wX8NV1ww8X", "reviewer": "Reviewer_uBVc", "summary": "The paper presents a unified transformer-based framework for joint expressive performance rendering (EPR) and automatic performance transcription (APT). The model disentangles note-level score content from global performance style representations using both paired and unpaired data, trained solely with sequence-level alignment. One of the contributions is brought by a diffusion-based performance style recommendation (PSR) module that generates style embeddings directly from the score, ensuring stylistic coherence and enabling flexible style transfer. Another contribution includes a seq2seq formulation of EPR without note-level alignment which avoids the need for finely aligned training data. Objective and subjective evaluations demonstrate that the proposed framework achieves competitive performance on EPR and APT tasks, while disentangling content and style to support reliable and stylistically consistent rendering.", "questions": "1. Why statistical relevance of the results is not included (table 1 to 3) ?\n2. The score encoder f_g,X(.) is introduced in section 3.4. What is the difference of this score encoder from the content encoder for score f_c,X(.) ? is this additional notation needed ?\n3. The disentanglement of score content and performance style representations is claimed but not really clear. What in the method facilitates the disentanglement ? (in other word, why distanglement is achieved)  ?\n4. It does not seem obvious to conclude from figure 4 that the PSR generated styles closely mirror those extracted from real performances. Can you further elaborate why this can be concluded ?\n5. What is the complexity of the model (training and inference) compared to baselines ?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The main strengths of the paper are:\n- The proposal of a unified model for joint expressive performance rendering and automatic performance transcription is novel and sound, although the concept of performing joint tasks (such as recognition or synthesis) is not new\n- The paper is nicely written\n-   Experimental plan is solid and includes comparisons with several baselines, and a perceptual test\n- The (online) demo is convincing", "weaknesses": "The main weaknesses of the paper are:\n-\tThere are no evidence that the results of the proposed method are statistically different from those obtained by the baseline methods\n-\tThe perceptual test is conducted on only five pieces, which limits the generalizability of the results across a broader range of styles, despite the examples being drawn from different composers.\n-\tWhy there is disentanglement of score content and performance style representations is not clearly described.\n-\tThere is no figure of complexity of the model (training and inference)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)