LadderSym: A Multimodal Interleaved Transformer for Music Practice Error Detection
Authors: Benjamin Shiue-Hal Chou
Purvish Jajal
Nicholas John Eliopoulos
James C. Davis
George K Thiruvathukal
Kristen Yeon-Ji Yun
Yung-Hsiang Lu
Venue: ICLR 2026 Poster (2026)
Keywords: Music
Audio
Multimodal learning
Representation Learning
Transformer
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We achieve state-of-the-art performance for music performance error detection with a new architecture.

Abstract: Music learners can greatly
benefit from tools that accurately detect errors in their practice.
Existing approaches typically compare audio recordings to music scores using heuristics or learnable models.
This paper introduces \textit{LadderSym}, a novel Transformer-based method for music error detection.
\textit{LadderSym} is guided by two key observations about the state-of-the-art approaches: (1) late fusion limits inter-stream alignment and cross-modality comparison capability;
and (2) reliance on score audio introduces ambiguity in the frequency spectrum, degrading performance in music with concurrent notes.
To address these limitations, \textit{LadderSym} introduces (1) a two-stream encoder with inter-stream alignment modules to improve audio comparison capabilities and error detection F1 scores, and (2) a multimodal strategy that leverages both audio and symbolic scores by incorporating symbolic representations as decoder prompts, reducing ambiguity and improving F1 scores.
We evaluate our method on the \textit{MAESTRO-E} and \textit{CocoChorales-E} datasets by measuring the F1 score for each note category.
Compared to the previous state of the art, \textit{LadderSym} more than doubles F1 for missed notes on \textit{MAESTRO-E} (26.8\%~$\rightarrow$~56.3\%) and improves extra note detection by 14.4 points (72.0\%~$\rightarrow$~86.4\%).
Similar gains are observed on \textit{CocoChorales-E}. Furthermore, we also evaluate our models on real data we curated. This work introduces insights about comparison models that could inform sequence evaluation tasks for reinforcement learning, human skill assessment, and model evaluation.

Published as a conference paper at ICLR 2026
LADDERSYM: A MULTIMODAL INTERLEAVED TRANS- FORMER FOR MUSIC PRACTICE ERROR DETECTION
Benjamin Shiue-Hal Chou', Purvish Jajal', Nick John Eliopoulos' James C. Davis', George K. Thiruvathukal, Kristen Yeon-Ji Yun', Yung-Hsiang Lu' 'Purdue University
LLoyola University Chicago
fchou150, pjajal, neliopou, davisjam, yun98, yunglu yu wpwruuee .ddu gkt luc.edu
ABSTRACT
Music learners can greatly benefit from tools that accurately detect errors in their practice. Existing approaches typically compare audio recordings music scores using heuristics or learnable models. This paper introduces LadderSym, a novel Transformer-base method for music error detection. LadderSym guided by two key observations about the state-of-the-arr approaches: (1) late fusion limits inter-stream alignment and cross-modality comparison capability; and (2) reliance score audio introduces ambiguity in the frequency spectrum, degrading per- formance music with concurrent notes. To address these limitations, Ladder- Sym introduces (1) two-stream encoder with inter-strean alignment modules improve audio comparison capabilities and error detection Fl scores, and (2) a multimodal strategy that leverages both audio and symbolic scores by incorporating symbolic representations as decoder prompts, reducing ambiguity and improving F1 scores. We evaluate our method the MAESTRO-E and CocoChorales- datasets by measuring the Fl score for each note category. Compared to the previous state of the art, LadderSym more than doubles Fl for missed notes on MAESTRO-E (26.8% - 56.3%) %) and improves extra note detection by 14.4 points (72.0% 86.4%) Similar gains are observed on CocoChorales-E. Furthermore, we also evaluate our models on real data we curated. This work introduces in- sights about comparison models that could inform sequence evaluation tasks for reinforcement learning, human skill assessment, and model evaluation.
INTRODUCTION
200
200
Missed
Wrong
Extra
Reference Music Score
Practice Mistakes
Figure 1: The error detection task for music practice. Given a reference score and a performance, solutions must detect three types of errors: extra notes; missed notes; and wrong notes. Wrong notes are represented as both missed note and an extra note.
Effective skill assessment accelerates human skill acquisition by providing targeted feedback (Sigrist al. 2013). Music learners, in particular, benefit from tools that surface practice mistakes (Apaydınh 2019). Yet over 4 million U.S. K-12 students lack access to music education (Morrison et al. 2022 highlighting the need for accessible feedback tools. Music practice error detection addresses this need by comparing practice recording to reference music score, as illustrated in Figure 1 where the score may be provided in symbolic or audio form.
Commercial apps for music education, such as Yousician (YousicianLtt, 2024 and Simply Pi-
ano (JoyTunez 2024), are widely used with over 10 million downloads each. However, these systems

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offer only coarse correctness judgments (e.g., whether note is correct) and do not classify ertor types such as missed, extra, or wrong notes. This limits the quality of feedback. In contrast, recent research attempts to provide finer-graind feedback by aligning student practice audio with symbolic reference scores (Benetos et al. 2012 Wang et al. 2017). Chou et al. (2025) found that such alignment-base methods break down when performance deviates substantially from the reference, limiting their reliability for error detection. To address this, Chou et al. (2025) adapted transformers (Vaswani et al. 2017), achieving superior Fl scores. Their model compares practice and reference recordings in the latent space, eliminating the need for explicit alignment. Despite these advances, we observe two key limitations in this of-e-of-thhe-rr approach. (1) The model uses late fusion by combining the two audio streams with single joint encoder layer. Through ablations and attention map visualizations, we show that this design limits alignment capacity. Stacking multiple joint layers improves align- ment but restricts asymmetric feature extraction. (2) The score is represented only as audio. This introduces ambiguity about which notes are present, especially when multiple notes are played at once. Overlapping frequency content makes it difficult to distinguish individual notes.
We introduce LadderSym, a new architecture that addresses both limitations. To address limitation (I), design Ladder two-stream encoder that shifts alignment inter-stream alignment modules via inductive bias. This allows standard transformer encoder layers to focus on feature extraction without being forced to share capacity for alignment. To address limitation (2), incorporate both audio and symbolic representations of the score. The symbolic score, denoted as Sym, refers to the tokenized version of the music score. This symbolic score is provided the decoder as a prompt, while the audio score is processed through the encoder and serves as context. This design reduces ambiguity of score inputs.
LadderSym achieves state-of-the-art performance on both the synthetic MAESTRO-E and CocoChorales-E datasets. On MAESTRO-E, it more than doubles F1 for Missed Notes (26.8% + 56.3%) and improves Extra Note detection by 14.4 points (72.0% + 86.4%). On CocoChorales-E, it improves Missed Note from 51.3% to 61.7%, and Extra Note from 46.8% to 61.4%. We also validate our model's s generalization on a new, largest to date dataset we curated of real-world beginner performances. Demo examples of model outputs are available at: Jour demo page
Our contributions are:
1. We develop novel encoder architecture that improves comparison by aligning audio representations frequently across input streams ($..).
2. We improve model performance with a multimodal strategy, by prompting the decoder with symbolic music inputs and reducing the ambiguity of its inputs ($3.2).
3. We analyze transformer attention patterns to extract design principles for cross-modal comparison and apply them to improve model performance ($4.3.1 Appendix A.9)
Significance: The high accuracy of LauderSSm takes a step toward solving the field's "chicken-and- egg" data problem (Kirillov et al. [0023] Monarch 2021): its potential as assistive annotation tool can help create real-world datasets needed to train the next generation of models(see $A.7.2 for our inmtan-i--the-looo workflow). Annotating just 20 pieces required approximately 52 person hours, and scaling further would demand hundreds more. More broadly, our work focuses on music practice error detection, which fundamentally an evaluation task that involves aligning and comparing two inputs. The architectural insights from this paper could inform the design of more effective evaluation methods in other domains, such as reinforcement learning, other human-skill assessment, and model evaluation. We elaborate on these implications in $5
2
BACKGROUND AND RELATED WORK
We review music practice error detection ($2. and common multimodal design strategies ($2.2)
2.1 ERROR DETECTION FOR MUSIC PRACTICE
Music error detection (Figure is an instance of the sequence-ter seuuence-to-sequeeee to-ueueence learning prob- lem (Sutskever et al. 2014 Luong et al.) 2016 Hawthorne et al.) 202T). Specifically, it
"The name reflects our goal help students "climb the ladder" of music skill development.
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Score
Score-Audio Alignment
Tokenize as Prompt
Audio
Compartson
Errors
Transcription
Synthesized Audio
(a) Explicit alignment.
Score
Enc
Score
Synthesized Audio
Enc
Joint
Cross-Attention Modules
Dec
Errora
Dec
Errors
Audio
Enc
Enc
Audio
Enc
Latent alignment staae-of-thhe-ne
LadderSym (Ours).
Figure 2: Comparison of error detection architectures. (a) Explicit alignment methods align the score with audio and compare it to the transcribed practice (Benetos et al., 2012). (b) Latent alignment methods synthesize the score audio and pass it to the encoder directly, without explicit alignment (Chou et al., 2025). (c) Our method, LadderSym, is a latent alignment approach that incorporates symbolic score prompting to address score ambiguity and enhanced cross-stream information flow via cross attention modules.
many-to-one sequence translation task, as it relies on two sequences (practice and reference, audio or symbolic) that compared to produce eπrо sequence. There two main approaches to music practice error detection: explicit alignment and latent alignment, illustrated in Figure 2
Explicit alignment methods compare transcribed notes from the score and practice audio (Figure 2 Techniques such as Dynamic Time Warping (DTW) (Sakoe & Chibal 1978) align the score and practice audio to facilitate this comparison. These methods identify differences by comparing the symbolic score to reference audio (Benetos et al. 2012 Wang et al. 2017). However, DTW is sensitive to deviations from the reference sequence, commonly present practice recordings with errors, leading to inadequate error detection (Chou et al. 2025).
In contrast, latent aligument methods forgo explicit sequence aligmment and instead learn directly output mistakes by comparing the score and practice streams (Figure Polytune (Chou et al. 2025) pioneered this direction, coupling an Audio Spectrogram Transformer encoder with a T5 decoder to align synthesized score audio with the practice recording. Latent alignment delivered clear gains: compared to an explicit-alignment baseline, Polytune raised Missed-note Fl on MAESTRO-E from 6.6% to 26.8% and Extra-note Fl from 39.9% to 72.0%. Although Polytune is the state of the art, its performance still low on missing notes, and its alignment behavior is not yet well understood.
Our proposed method, LadderSym (Figure 2). builds upon the latent alignment paradigm but introduces two key innovations to address these limitations. First, as shown in the top-right of panel (c), incorporate symbolic representation of the score a prompt the decoder. This provides clear, non-ambiguous reference that bypasses the overlapping frequency issues inherent in audio-only scores. Second, we introduce novel encoder with inter-stream alignment modules. Unlike the late-fusion approach in Polytune, which aligns streams only in the final layer, our architecture allows for the decoupling of feature extraction from alignment via alignment module every layer.
2.2 DESIGN OF MULTIMODAL ENCODERS
Our work addresses these limitations by drawing on principles from multimodal encoder design. Multimodal models handle multiple input modalities or different representations of the same modality (e.g., RGB and depth maps). They use either single-stream encoder (early fusion) or separate, parallel encoders with fusion layers that enable cross-modal interaction. BBaltrusaitis et al. (2019) identify three dominant paradigms for such fusion: early, late, and hybrid. Early and late fusion appear more often when training from scratch (early fusion (Girdhar et al., 2022 [L et al. 2019): late fusion (Ao et al.
2022
Gong et al. 2023 Akbari et al. [022 Girdhar et al. 2023 Radford et al.
2021
Chen et al.
2023 hybrid fusion (Alayrac al. [022]] Li et 2022)).
Hybrid fusion occupies the middle ground between early and late fusion. One approach to use cross-attention condition encoder the output of another encoder. Recently, hybrid designs
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most commonly arise when adapting language models (LMs) for vision tasks. The prevailing strategy
is to pair a pre-trained Vision Transformer (ViT) with a (usually frozen) language model and condition
each LM layer on external modality information (Alayrac et al. 2022 Li et al. 2022). with an adapter
inserted between the final vision encoder layer and every LM layer.
In this paper, we introduce novel hybrid fusion approach that encourages alignment be handled by inter-stream alignment modules while decoupling feature extraction across modalities. Our design still uses cross-attention since it can be used to pass information between token streams of varying length with minimal impact on latency (Jajal et al. 2024 Koner et al. 2024), but it diverges from two common hybrid strategies. First, unlike conditioning approaches such as CLIP (Radford et al. 2021). where interaction often occurs just once on compressed, static vector, our model performs iterative, layer-wise cross-altention on the full sequence of features. Second, this allows our architecture to implicitly learn tite-grained temporal alignment. This contrasts with hybrid temporal models that assume alignment a priori, for instance by interleaving multimodal tokens the input layer (Xu et al.) 2025). The resulting learned alignment mechanism is key for complex comparison tasks like music error detection and may generalize to other fine-grained comparison problems, as we discuss in $5
METHOD
This section details LadderSym, our multimodal transformer for music error detection. Our design revolves around two questions raised by the analysis in 12.1 (1) where should alignment occur between score and practice streams? (2) how can we reduce the ambiguity introduced by representing the score solely as audio?
To answer these questions we study the behavior of existing architectures. We probe the representa- tions learned by late- and carly-fusion encoders, quantify how fusion depth affects performance, and analyze how decoder inputs influence missed-note detection (Tables I clarify how much cross-stream interaction is necessary and highlight the information lost when the decoder lacks symbolic context.
[)d These measurements
Guided by these findings, we construct LadderSym, shown in Figure 3 The Ladder encoder is two-stream transformer whose cross-attention modules precede each layer, aligning score and practice audio representations while decoupling feature extraction from alignment ($3.1). The Sym prompt supplics symbolic score tokens to the decoder ($3.2). reducing reliance on ambiguous audio-only cues and improving detection of subtle errors such as missed notes. We summarize model I/O, architectural configurations, and other implementation details in $3.3
Input
$3.2 Stage 2: Prompting the Decoder
Synthesize Audio
Music Score
Token Embedding
Prompt Input
Past Tokens
Score
Practice
Encoder Output
Concatenation
Projection
Decoder Input Tokens
Input Patchificatiin
Input Patchificatiin
$3.1 Stage
Ladder
Decoder
Align & Mix
Encode
Encode
Align & Mix
Oulput
Align &
Missed
Wrong
Align Mix
Encode
Encode
Concatenation
Extra
Figure 3: Architecture of LadderSym. We feed both score audio and practice audio into Ladder, our novel encoder with inter-stream alignment modules. Their latent features are concatenated and used as context for the autoregressive decoder. To create LadderSym, we prepend symbolic prompt that is generated from a MIDI version of the score before the start-of seqqueeeee token to provide a different representation of the reference score. The T5 decoder then produces MIDI-like tokens, labeling notes as correct, missed, or extra.
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3.1 STAGE 1: THE LADDER ENCODER
Motivation:
Ladder aims overcome key limitation of the state-of-the-art Polytune architecture: its late fusion design lacks interaction between the practice and score inputs. Our ablations show that fusing earlier enhances interaction between reference and practice inputs, improving performance (Table Thus, we conclude that inter-stream information flow is beneficial to our model's ability to compare the inputs. However, fusing too early harms performance, as shown when using more than three joint encoders (Table D Attention maps (Appendix A.9 reveal that late fusion limits alignment and comparison between inputs. Early fusion enables alignment in initial layers but sacrifices cross- stream feature extraction due parameter sharing (Table To guide encoder design, first probe (Raghu et al., 202T the latent representations of two baseline architectures: Polytune and an early fusion encoder. Each encoder is frozen, and train probes to evaluate locality and globality of the learned representations. Locality is measured by whether each stream retains token-level temporal position information and globality is measured by coarse clip-level energy information. In Polytune, the practice stream maintains strong locality (0.86), while the score stream shows reduced local accuracy (0.45) but improved global awareness (0. 179 - 0.186). This pattern suggests division of labor: one stream specializes in local detail, while the other encodes global features. In contrast, the early fusion encoder yields high locality in both streams (0.91 and 0.93), along with balanced global information. This is because both streams share parameters. We hypothesize that this limits the streams' ability specialize, which intuitively can harm comparison performance, as comparing A to B should be redundant with comparing B to A.
This motivates a design that combines the strengths of early and late fusion. Our model similar to late fusion in that its design uses separate encoders for each input stream. One encoder extracts local features, while the other captures global features. Unlike late fusion, our architecture supports interaction at each layer for fine-grained alignment, similar to early fusion. This novel design enables effective cross-modas modal comparison without being constrained by cross-modal parameter sharing.
Architecture: Our design for LadderSym consists of novel interleaved encoder architecture, which we designed based on intuitions about comparison tasks. Before each transformer block, one input stream aligned and additively fused into the other. The cross-attention alignment module (1) enables alignment at each layer and (2) allows the transformer blocks to focus on feature extraction. As shown in Figure 4 the learned attention maps recover the same off-diagonal structure DTTW alignments, demonstrating that corresponding tokens across time learn to attend to each other. Attention maps for deeper layers shown Figure
Alignment Module Layer 1
DTW
Time Score
Time Score
Time Practice
Time Practice
Time IMM
P
(a) DTW path between a normal and a time-compressed sine wave: The off-diagonal line shows how DTW stretches time to align the two se-
(b) Cross-attention map from the alignment module: The x- and y-axes denote time in the score and practice spectrograms, respec- tively. Attention map values averaged over the pitch dimension (P) to highlight temporal alignment (T). For the attention map on the right, we shift the score forward by 0.5 seconds. We can see that the model's attention shifts to the upper left.
quences.
Figure 4: Similarity between (a) Dynamic Time Warping and (b) learned alignment patterns the alignment module. This visual similarity suggests that the alignment module is successfully learning a mcaningful temporal correspondence between the two audio streams, analogous to the explicit alignment path found by classical algorithms like DTW.
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The process for encoder block is described by:
P 1(+1) 1)
ViT (p()
+ CA(P (
P(f)
(1)
P ((+ VIT prac
P( CA (P (1+1) P(()
(2)
Here, Prer represents the score audio embeddings, P
prue the practice audio embeddings, CA the
cross-attention operation, and the current layer index. The final fused representation obtained as:
Hused Concat (P (linal) P ((fiiaa))
prac
The alignment module combines cross-attention and additive fusion to to first align then pass information between streams each layer. Additive fusion means directly adding cross- attsentntnti output to the stream embedding, preserving both self and aligned information from the other stream. This fused representation is then processed by standard ViT block. We then reverse the alignment and fusion direction and pass the result through the next ViT block. Finally, we concatenate both final states into fused latent Hused
We illustrate the encoder block in Figure 5
The alignment module is summarized by the expression:
(
+
CA(P
.P()) (
pruc
rel
prac!
where the cross-attention output is directly added to the current representation before being passed to the next encoder block.
Encoder Block
Score Encoder Block
KV
N
CA
CA
Practice Encoder Block
KV
Figure 5: Topology of the encoder block: Alignment modules alternate between streams, allowing iterative alignment and fusion of information from the score and practice audio. The encoder blocks process the intermediate representations.
Table 1: Probing frozen encoders. To analyze the representations learned by each encoder, the model frozen and evaluated using lightweight linear probes. Locality predicts a token's 's temporal position, globality predicts clip-level energy label, and cross-stream correspondence uses one stream to predict the other's ' energy. For Polytmee, we extract features before the joint encoder layer, so cross-stream probes are not applicable. All values are accuracies (see Appendix A. for setup details).
Local score
Local practice
Global score
Global practice
Cross-Stream practice to score score to practice
Cross-Stream
Model
Polytune
0.452
0.862
0.186
0.179
Early fusion
0.909
0.931
0.292
0.273
0.280
0.269
LudderSym
0.197
0.681
0.162
0.252
0.158
0.300
Probing Ladder: Having introduced the interleaved encoder, we return to the probing framework to assess how this design shapes latent representations. Using the same probing setup, we now evaluate LadderSym in terms of locality, globality, and cross-stream correspondence, Results are shown in Table I We find that the practice stream retains strong local information (0.681), and the score stream has reduced locality (0.197). However, the score stream encodes cross- stream features from the practice stream more accurately than any prior model (0.30). These results confirm that LadderSym supports asymmetric division of labor between streams, enabling specialization.

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3.2 STAGE 2: HARNESSING SYMBOLIC SCORES BY PROMPTING THE DECODER
We give the decoder direct access to symbolic score information via prompting to leverage the complementary strengths of symbolic and audio representations. Symbolic-only tokenizers can introduce alignment errors, especially in complex time signatures (Fradet et al. 2021). while audio representations often suffer from overlapping frequency bins that obscure concurrent notes. Providing both views helps mitigate these respective weaknesses. Table [ shows that using prompting strategy on Polytune (Prompt + Audio) can significantly improve performance over just using audio inputs (Audio Only). We also test variation of Polytune and find that Audio Only outperforms using only the prompt (Prompt Only). Table 5 also shows that combining our prompted decoder strategy with the encoder that includes inter-stream alignment modules yields the highest scores for all categories in MAESTRO-E and for missed notes in CocoChorales-EE We use a T5 decoder following Chou al. 2025).
3.3 IMPLEMENTATION DETAILS
Input/Output Format: To tokenize the input audio spectrogram, we follow the procedure in
MT3 (Gardner et al. 2022) and Polytune. The output format also follows (Chou et al.) 2025), which is modified version of (Gardner al. 2022), omitting instrument tokens (assuming single- instrument setting) and adding explicit error labels (ext miss ssed, correct). Further details for both are presented in Appendix A.2 and Appendix A.3 A detailed breakdown of our prompt tokenization scheme is provided in Appendix A.2.2 for interested readers.
Model Implementation: LadderSym has a configuration of 12 transformer encoder layers and decoder layers to match the layer count of the AST (Audio Spectrogram Transformer) (Gong et al.) 202I) encoder and T5 decoders used in (Chou et al., 2025). The transformer encoder output, with dimensionality of 768, is projected down to 512 to match the T5 decoder's dimensionality. Our training regime follows that of (Chou et al. 2025) and is detailed in Appendix [A.4] We use the same hyperparameters Polytune. We adapted model component code from Bfw.eefffiettiiii (Jajal ct al. (MIT License), though our approach differs in design. We also build on Polytune
(BSD 3-Clause, non-commercial).
4 RESULTS
We present results comparing LadderSym against Polytune (Chou et al. 2025) and an explicit- alignment baseline derived from Benetos et al. (2012) and [Wag et al. (2017). Evaluations cover CocoChorales-EE E, MAESTRO-E (Table 2). and small real-world dataset. Full experimental details
are in $4.1 Our evaluation includes: (1) A quantitative comparison showing improved Fl scores
all datasets (: (2) an ablation study analyzing different variants of the encoder ($4.3.) T); and (3)
an ablation study examining the effect of prompting with the symbolic music score (4...2
4.1 EXPERIMENTAL. DESIGN
Software and Hardware: We train and evaluate our models using PyTorch 2.3.0 and Transformers 4. 4.40.1 on an NVIDIA A100-80GB GPU.
Datasets: We follow Chou et al.] (2025) and evaluate on their partially synthetic benchmarks, MAESTRO-E (competition piano repertoire with dense chords) and CocoChorales-E (13 single- instrument tracks without overlap). Dataset construction and summary statistics appear in Ap- pendix A.6 All ablations use the combined synthetic test split (4401 tracks).
To test real-world robustness, curated 20-track beginner piano set: Three graduate students who are novice pianists each played 6-7 pieces. To our knowledge, this is the first dataset of genuine beginner mistakes. Each take pairs the reference MIDI score with practice audio and note-level annotations verified by two musically trained students. Per-piece metrics and additional details are in Appendix A.77 Every model is additionally evaluated this real-data set without finetuning.
Explicit-aligmmet baseline: This bascline extends Benetos et al. (012)) and Wang et al. (2017) with the more modern MT3 Gardner et al. (002). Implementation details appear in Appendix A.8
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Table 2: Comparison of LadderSym and Polytune across error types in two datasets, MAESTRO-E and CocoChorales-E. E. Error types are abbreviated (C Correct, M = Missed, = Extra); CocoChorales metrics are macro-averaged across instruments. Blue highlights mark LadderSym, pink highlights mark Polytune, and bold indicates the best value per column; colors are for visual emphasis only. The explicit-aligmmen baseline appears in the rightmost block for context.
LadderSym
Polytune
Explicit Align.
Dataset
E
M
E
C
M
E
MAESTRO-E
94.4%
54.7 %
86.4 %
90.1% %
26.8%
72.0%
43.5%
6.6%
39.9%
CocoChorales-E
97.7%
61.7%
61.4%
95.4%
51.3%
46.8%
36.7%
7.7%
23.5%
Metrics:
We use the evaluation metric Error Detection F1, introduced by (Chou al. 2025). Error Detection Fl measures the Fl score for Missed, Extra, and Correct notes.
4.2 QUANTITATIVE RESULTS
Main results: Fl scores for CocoChorales-E and MAESTRO-E are presented in Table 2 with per-instrument results available in Appendix A. We achieve across-the-bel board improvements. As expected, on the highly concurrent MAESTRO-E dataset, the performance gain is most notable, with the missed note F1 score improving from 26.3% to 54.7%.
Real- World Performance: To validate our model's s effectiveness beyond synthetic data, we also tested it on new, manually annotated dataset of real-world beginner performances. The model was not finetuned. On this authentic data, LadderSym shows a marked improvement in detecting missed notes (78.5% vs. 63.9 63.9% F1) and a small gain in extra note detection (81.6% vs. 80.6% F1) when compared to Polytune. While the overall performance gap is smaller due to the pieces being simpler than MAESTRO-E. LadderSymstill has better across the board performance. Most importantly, the results demonstrate that LadderSym's architectural improvements successfully generalize to genuine human errors. More details are in the Appendix (Appendix (.7))
Model size and speed: LadderSym (172M parameters) is more efficient than the state-of-the-art Polytune (192M parameters) despite its iterative alignment modules. By comparison, under identical settings on an A100-80GB GPU, our architecture reduces processing overhead at the encoder stage. As shown in Table LadderSym achieves a significantly lower encoder latency (#:97ms vs. 129ms) and improves the worst-case token latency by approximately 32ms. For more detailed breakdown of initial processing and generation overhead, please see Table 3
Table 3: Latency metrics for Polytune, LadderSym, and LadderBole indicates best performance;
underlined indicates second best.
Encoder latency (s)
Decoder 1st
Worst Case
Model
Token (s)
Token (ms)
Polytune
0.129 + 0.024
0.00786 + 0 0.0356
136.86 + 0.0596 104.97& + 0.0599
LadderSym
0.0971 = 0. 0.0398
0.00787 + 0.0201
Ladder 0.0972 0.0452
0.00801 + 0.0364
105.21 + 0.0816
4.3 ABLATIONS
In this section, we ablate two core design choices in LadderSym. We conduct ablations to answer the following questions: (1) How does fusion location affect performance ( ($4.3.12 (2) Which input
combination yields the best results in LadderSym ($4.322
4.3.1 THE EFFECT OF FUSION LOCATION ON LadderSym
We first examine how frequently the score and practice streams should interact inside the encoder. Starting from the Polytune architecture, we vary the number of joint encoder layers (Ljvin) while holding either the total depth or the number of modality-specific layers constant.
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Table Effect of joint encoders on Fl score, measured on CocoChorales-E. Left: Fixed total layer count to 12. Right: variant with fixed modality-speciff encoders. Best results per half are in bold. Performance peaks at two three joint layers, motivating our design that interleaves cross-attention while preserving mopelity-specific encoders.
Fixed Total Layers Missed
Fixed Modality Encoders
Ljoint
Correct
Extra
Correct Missed
Extra
1
95.39%
51.26%
46.80%
2
96.95%
59.58%
57.38%
97.00%
59.30%
56.70%
3
97.34%
56.81%
59.61% %
97.45 %
56.14%
57.83%
4
96.80%
59.51%
58.11%
96.95%
58.05%
55.57%
12 (Farly Fusion)
96.50%
54.60%
56.20%
Table 5: Ablations on input configurations and encoder design. We compare prompt-only, audio- only, and combined inputs for Polytune and LadderSym, along with encoder variants that change how the two streams interact. MAESTRO-E the more challenging dataset because of higher note concurrency, making gains there harder to realize. LadderSym attains the best scores on MAESTRO-E, while Ladder and LadderSym behave similarly on CocoChorales-E. Blue highlights mark LadderSym, pink highlights mark Polytune, and arrows indicate score trends; the styling is for emphasis only. These comparisons justify pairing the Ladder encoder with symbolic prompting in the final model.
MAESTRO-E
CocoChorales-E
Type
Method
Missed
Extra
Correct
Missed
Extra
Correct
Prompt Only
24.3%
62.5%
90.6%
44.6%
45.8%
89.4%
Input
Audio Only
26.8%
72.0%
90.1%
46.8%
51.3%
95.4%
Config
Prompt + Audio
46.7% f
81.7% f
93.7% f
56.1% f
58.1%
96.9% f
Joint Encoders
36.1%
75.3%
92.6%
56.8%
59.6%
97.3%
Encoder
Self-Attention
33.8%
74.6%
93.0%
54.6%
56.2%
96.5%
Design
Ladder
46.0% 1
82.0% f
93.7% f
61.0%
62.3%
97.8 %
Final Model
LadderSym
54.7%
86.4 %
f
94.4% f
61.7% t
61.4%
97.7%
To study fusion depth, we vary the number of joint encoders (Ljoine) while keeping the total encoder layers Logal fixed. The remaining layers are assigned to modality-specifii encoders (score and practice). The number of moddality-speciffe layers determined by (Lmod Liotal Ljoim)) As shown in T increasing Lisin improves Fl scores. However, gains diminish beyond 2-3 joint layers (Table 4 To isolate the effect of increasing joint encoders, also fix T-mod and vary Tijain- This also shows peak performance at 2-3 joint encoders, followed by a decline. This confirms that too many joint encoder layers lead diminishing returns, suggesting that there tradeoff between alignment capability and the ability to separately encode inputs.
4.3.2 ABLATION STUDY OF INPUT REPRESENTATIONS
In order to test the effectiveness of adding music scores symbolic prompts, we evaluate three input setups: Audio Only, Prompt Only, and Audio + Prompt for both Polytune and LadderSym. Table shows that Audio + Prompt outperforms both individual inputs. Symbolic prompts offer additional context for better detection. The upgraded Ladder encoder improves performance on its own, and combining it with symbolic prompting further boosts F1 scores. LadderSym achieves top scores overall but underperforms Ladder in CocoChorales-E by a small margin. We analyze this in $
5 DISCUSSION
Analysis:
Combining the improved encoder with the prompting strategy provides limited Fl improve- ments. As shown in Table (LadderSn vs Ladder), this is likely because both components enhance inter-input communication in overlapping ways. The encoder improves inter-stream interaction,

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while the prompt adds audio-symin sudio-yymbolic comparison capabilities. Although the prompted version of LadderSym does not outperform the unprompted variant in all categories of Coco E, it consistently achieves better results MAESTRO-E, which contains more challenging musical content (competition pieces). We therefore integrate prompts into the final version of LadderSym.
Limitations: We identify three main limitations. First, dense concurrency present in MAESTRO-E acoustically masks missed notes; although LadderSym more than doubles missed-note F1 relative to Polytune, that class remains the most challenging. Second, errors accumulate near segment boundaries when sustaining notes cross the context window; those tails can be mis-labelled as extra notes, which could be mitigated with a sliding window or memory mechanism. Third, the model designed for fine-grained alignment and is robust to local tempo deviations like rushed or dragged notes, but it is not intended to align performances that diverge dramatically in tempo (e.g. playing at half speed), In practical tutoring settings we expect users to practice near chosen tempo; if coarser tempo changes must be handled, a lightweight pre-alignment step can be inserted ahead of LadderSym.
Future Work: A key direction for future work is to address the "chicken-and-egg" problem. As demonstrated in the creation of our real-world validation set (Appendix (A.7), LadderSym can serve as a human-in-the-lowe larger dataset of authentic performance errors, enabling the training of models that are even more robust. Beyond this, LadderSym introduces two key insights: cross moos- alignment should happen frequently, and asymmetric feature extraction supports better comparison. These ideas can inform reward model design improve benchmarks for evaluating generative models. They can also guide skill assessment in other domains, such as evaluating speech or assessing physical movements.
annotation tool. A larger-scale effort could use this approach to build
6 CONCLUSION
The existing methods of music practice error detection can help more effective skill improvement, yet there remain challenges. Prior work suffers from core drawbacks: (1) late fusion design that restricts comparisons between practice and score streams, limiting detection Fl scores, and (2) relying on audio to represent music scores causes ambiguity. In this work, we introduced LadderSym to address these challenges through two key innovations: (1) new encoder architecture featuring alignment modules for improved inter-stream interaction, and (2) a symbolic score prompt that reduces the ambiguity in the reference music score. This approach achieves state-of-the-art F1 scores on both MAESTRO-E E and CocoChorales-E. On MAESTRO-E. improves Missed note FI from 26.8% to 56.3% % and Extra note Fl from 72.0 % to 86.4% % On CocoChorales-E. it improves Missed note F1 from 51.3% to 61.7% and Extra note F1 from 46.8% % to 61.4%. The model's performance on real-world beginner dataset confirms its practical utility for music learners. More broadly, this work introduces general architectural principles for sequence comparison. The insights could inform more effective models for other evaluation tasks, such as reward modeling in reinforcement learning, human skill assessment, and the benchmarking of generative models.
ACKNOWLEDGMENTS
This work was supported in part by NSF grants IIS-2326198 and OAC-2504445, the Purdue College of Engineering, and the Patti & Rusty Rueff School of Design, Art, and Performance. Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect the views of the sponsors.
Reproducibility Statement: We use the publicly available MAESTRO-F and CocoChorales-F! datasets, with the creation process described in $A.6 Our newly curated real-world evaluation dataset is Experimental Setup: Our training procedure, including all hyperparameters, is specified in $ A.4 and Table 7 The evaluation metrics and statistical analysis methods are described respectively. We used a fixed seed for all experiments as documented in A..12
detailed in $A.77
in $4.1 and A.
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A
APPENDIX
This appendix supplements the main text with additional background and experimental details. $A.1 describes our probing protocol and A.. detail model inputs and outputs. $A.4ooutlnes training settings, A.5 defines evaluation metrics, and covers dataset generation. $A.7 describes the real-world collection and per-piece results. A..8 reviews the explicit-alie aplicit-alignmen baseline, $A.9 visualizes attention behaviors, repoorts per-instrument results, $A.1T presents statistical tests, and A. documents reproducibility practices. $A.13 lists LLM usage.
A. PROBING SETUP
Probes are trained for 25 epochs on the MAESTRO-E test Locality is defined predicting each token's ' position in a 16 x 32 pitch-time patch grid. Globality is measured by predicting a 12-bin energy label based on the token with the highest norm in each clip. Cross-stream correspondence is evaluated by predicting the energy bin of the opposite stream.
A.2 MODEL INPUTS
A.2.1 AUDIO
Our tokenization of audio inputs follows MT3 (Gardner et al. 2022) and Polytune. We segment each audio recording into 2.145-secone non-overlapping segments the short-time Fourier transform (STFT) with a 2048-0000 1448ppoint FFT, a 128- 128-sample hop, and 512 mel- bins. Each spectrogram frame is split into 16x16 patches using the Vision Transformer (ViT) patch embedding method (Dosovitskiy al. 2021), yielding 512 tokens per segment for each stream.
compute spectrograms using
A.2.2 SYMBOLIC SCORE PROMPT
As our T5 decoder is autoregressive, the symbolic score provided as an input prompt uses the same vocabulary as the model's output. The token types are described in Table
Table 6: Token types used in the symbolic score prompi and model output.
Token Type
Description
SOS / EOS
Start/End of Sequence.
Time
Specifies note timing within segment.
On / Off
Indicates whether a note is played or released. Mistake type. In the input prompt, all notes 'Label=Correct'. The MIDI pitch of the note.
Label
Note
The prompt is the tokenized symbolic score. For example, a single middle C (MIDI pitch 60) played correctly at the start of segment would be represented as follows:
[sos, Time=C, Label=Correct, On, Note=60, ...]
A.3
MODEL OUTPUT
Our model produces a stream MIDI-like tokens describing each musical event's time, pitch, play- back state, and error category. For example, sequence with two errors errors-"extra and "missed" looks like:
[SOS, Timo=C, Label=Extra, On, Notc=60, Time=3, Noto=60, off, Time=7, Label=Missed, On, Note=64, Time=9, Noto=64, Off, EOS]
Here, Time=0, Label=Extra starts the first erroneous note, and Time=3, Note=60, Off
indicates its deactivation. Subsequent tokens (Time=7, Label-Missed, On, Note=64) mark the onset of missed note four time steps later, followed by off to end it. Finally, EOS
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concludes the event sequence. Repetition errors follow the same schema. For example: if the score specifies A + B + C' but the performer plays A - A - B - C, the second is emitted as for the duplicate
Label=Extra; if they skip B entirely, the timeline contains both and Label =Mi ssed for B.
A.4 TRAINING
The model is trained end-to-end using score audio, practice audio, and a symbolic score prompt. Our training recipe largely follows that of Polytume (Chou et al., 2025), with key adaptations to improve performance and efficiency. We apply weighted cross-entrops loss mitigate the class imbalance between correct and missed/extra notes. To further improve generalization, we adopt token shuffling (Tan et al., 2024). which permutes output tokens without altering underlying semantics. Learning rates are adjusted using cosine annealing (Loshchilov & Hutter, 2017). starting at 2 X 10-4 and decaying to 1 x 10-4, Optimization performed with A (Loshchilov & Hutter 2017). All models are trained for 300 epochs using the largest batch size that fits on single NVIDIA AT00- 80GB GPU: 48 spectrograms per batch for MAESTRO-E and 96 for CocoChorales-EE The smaller batch size for MAESTRO-E reflects its higher note density and memory footprint. Training uses mixed-precision (bf16-mixed) to balance efficiency and numerical stability. Full hyperparameters are listed in 7able
Table Training hoperparanneters for each dataset. Batch size refers to the number of spectrogram segments per update.
Hyperparameter
MAESTRO-E CocoChorales-E
Number of Epochs
300
300
Learning Rate
2e-4 - le-4 (Cosine)
Batch Size (spectrograms)
48
96
Error Loss Weight
10
Scheduler
Cosine Annealing
Optimizer
Adam
Data Augmentation
Token Shuffling
Precision
bf16-mixed
A.5 METRICS AND EVALUATION
Error detection metrics have varied across studies. Benetos et al. (2012); Wang et al. (2017) consider a note prediction correct its onset falls within a specific timing tolerance relative to the ground truth. However, in this paper, we also require the pitch of the note to match, as specified by the mir_eval package (Raffel et al. 2014). Furthermore, the mir_eval package uses a 50 ms tolerance to calculate Fl overlap scores. In contrast, older metrics like MIREX onset accuracy employed different timing tolerances, such as 100 ms (Benetos et al. 2012) and 200 ms (Wang et al., 2017). These varying tolerances complicate direct comparisons, higher tolerances tend inflate accuracy scores. We use Error Detection F1 introduced by (Chou et al., (2025) because the more stringent 50 ms tolerance from mi r_eval and the ability evaluate each error category separately. This provides a more precise evaluation of model performance.
A.6 TRAINING DATASETS
Training an end-to-end model for music error detection requires a large volume of labeled performance mistakes. Yet, no large-scale datasets are available for this task. The only prior dataset, introduced by Benetos et al. (Benetos et al. 2012), contains just 7 tracks. To address this limitation, Chou et al. (2025) developed two new datasets: MAESTRO-E and CocoChorales-E, each containing over 1,000 samples per instrument. MAESTRO-E provides more than 200 hours of piano audio across 1,000+ tracks, annotated with 200k pitch and timing errors. CocoChorales-EE spans 300+ hours of audio with over 40,000 tracks and 13 instruments, capturing more than 25,000 annotated errors. In contrast, the dataset from Benetos al. includes only 15 minutes of audio, 7 tracks, and 40 labeled
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errors. To generate these datasets, MIDI samples from the MAESTRO and CocoChorales corpora were augmented with typical practice mistakes such as missed, incorrect, and additional notes. The corresponding audio was synthesized using MIDI-DDSP (Wu et al. 2022).
Training labels were defined by segmenting each augmented MIDI file into three separate MIDI tracks labeled Correct, Missed, and Extra, following the definitions introduced in $
Algorithm 1 MIDI error generation algorithu. This procedure introduces missed notes, pitch changes, timing shifts, and extra notes into a clean MIDI file. Reproduced from Chou al. (2025).
Require: All notes in MIDI track A, eтrоr rate X, offset distributions P, Q.
Select notes from
augment with probability À
2: for each note selected do
3: err_type <- rand( {missed note, pitch change, timing shift, extra note)))
4: if' enr_type missed note then
5:
Remove note;
6: else if err.type pitch change then
7:
Cp f sample(P))
8:
Offset pitch by Spi
9:
else err ter_ttpe--a = timing shift then
6t sample(Q)
11:
Offset time by ex:
else if err type extra note then
13:
Cp f sample(P)
G f sample(Q)
15:
Insert note with time offset and pitch offset epi
16: end
17:
end for
The error injection process is outlined in Algorithm Notes from MIDI track randomly selected with probability determined by X. which is sampled from uniform distribution (0011, 0.4). Each selected is then assigned an error type. Depending on the error, the note is removed, modified in pitch or timing, or new note is inserted with sampled pitch and timing offsets. The offsets are drawn from two truncated normal distributions and Q, centered at zero with standard deviations of I and 0.02, respectively. These distributions are chosen to reflect realistic variations observed human eerformaneeTrrmmmerhhie resulting datasets is presented in Table
al. 2005: Tibshirani et al. 2011). An overview of the
Table 8:
Key properties of music practice error-detection datasets. Each truck contains multiple missed or extra note errors and randomly timed timing perturbations.
Dataset
Tracks
Instruments
Errors
Source
MAESTRO-E
1k+
Piano
200k+
Gariaall-SSnthetti
CocoChorales-E
40k+
13
25k+
Partially-Synthee
Benetos et al.
7
Piano
40
Professional
Our real-world dataset
Piano
161
Beginners
A.7 REAL-WORLD EVALUATION DATASET
This section documents the cuttof-distribution benchmark we curated to complement the partially synthetic corpora. Table summarizee per-piece Fl scores, and Appendices A.7.I to A.7.3 describe how we recorded the performances, annotated them, and assessed model behavior on this set.
A.7.1 DESCRIPTION OF WORLD DATASET
While large-scale synthetic data crucial for training, a key limitation of prior work the reliance synthetic or scripted mistakes for evaluation, which may capture the full complexity and
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nuance of real human performance. Collecting dataset of authentic errors is time-consuming and challenging task. Unlike generating synthetic data or pretending to make a mistake, it requires capturing genuine mistakes made by musicians during the learning process. This involves finding beginner musicians and allowing them to practice on the spot, a process necessary to record the natural, unscripted errors. Furthermore, annotating these errors even more difficult, it requires fairly well-trained car to discern subtle inaccuracies. This need for domain expertise makes the task ill-suited for general crowdsoureing platforms.
To complement the partially-synthetic benchmarks, we captured a held-out corpus of beginner performances recorded on the piano by beginners (statistics shown in Table B). The dataset was acquired by capturing the direct audio output from a digital piano setup, which utilized VST (sampled from a Yamaha Disklavier). This method ensures a clean signal and consistent recording conditions. Each take includes the reference MIDI score, practice audio, and manual note-level labels. Because some pianos offers symbolic MIDI ground truth with well-defined onsets, annotators could compare MIDI labels rather than transcribe every event purely by car before comparing, keeping dataset labeling efforts feasible at a small scale. The ground truth annotations contain 75 wrong-nott pairs (a substitution of one note for another), 51 extra notes, and 35 missed notes, making isolated missed notes the rarest error category. To our knowledge, this is the largest publicly available dataset of real-world, annotated beginner performances curated specifically for music error detection, representing an almost threefold increase over previously existing real-data benchmarks. This expansion provides the community with a much-needed resource to validate models against real-world data.
Recording sessions featured three beginner pianists: one outside the author list who studied piano for only a year in childhood, one coauthor with no formal training, and one who plays guitar but has minimal keyboard experience. We curated twenty popular beginner- easy pieces and asked performers to play on the spot with minimal rehearsal so that mistakes arose naturally. Labels were produced by a author together with another student classically trained in sight-reading.
Assembling this dataset was far from trivial. Each participant recorded six to seven pieces under supervision, yielding total of 12 hours of recording and practicing time, and annotation required multiple passes by musically trained reviewers to verify every note-level label, taking up to approxi- mately 52 person hours to annotate. Scaling beyond 20 pieces would require hundreds hours of additional expert time, the dataset remains modest in absolute size. Crowdsourcing a comparable resource remains an open problem: most potential contributors lack access to recording setups and sufficient musicianship label fine-grained mistakes. Yet, to our knowledge, this corpus is the largest publicly available set of authentic beginner errors, and it provides a valuable resource to assessing whether models trained on synthetic data generalize to real practice.
A.7.2 LADDERSYM AS AN ANNOTATOR
To accelerate the laborious process of manual annotation, we use LadderSym as an assistive labeling tool. The model generates a first pass of annotations which, while not perfect, provides strong starting point. These preliminary labels, including any errors made by the model, are then meticulously reviewed, corrected, and verified by two human annotators to establish the final ground truth. This nhuman-in-the--e
approach is significantly faster than labeling from scratch. By shifting the task from pure annotation to verification and correction, we estimate that this process cut the required labeling time in half, speeding up throughput by 3x.
A.7.3 EVALUATION RESULTS
We evaluate all models on this dataset without additional finetuning. On this real-data set, extra notes are generally easier to detect as they stand out more in these simpler beginner pieces compared to the dense, complex arrangements in competition dataset like MAESTRO-E. Despite the challenges of real-world data, the relative performance ordering observed on synthetic data persists. LadderSym shows significant improvement in detecting the more challenging missed notes and also demonstrates a modest improvement in extra note detection over Polytune, highlighting its robustness. Per- Per-piece metrics are shown in Table 9
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Published as a conference paper at ICLR 2026
Table 9: Per-piece Fl comparison the real-data evaluation set. All models are trained solely on the synthetic datasets. Values are percentages; indicates the metric is undefined because the practice recording contains no events of that type.
LadderSym
Polytune
Piece
Extra
Missed
Extra
Missed
Amazing Grace
92.3
76.9
100.0
76.9
Für Elise
80.0
88.9
62.5
53.3
Greensleeves
83.3
72.7
90.9
60.0
Morning (Grieg)
100.0
75.0
100.0
44.4
Happy Birthday
100.0
66.7
100.0
66.7
Jupiter (Holst)
80.0
66.7
80.0
0.0
Jingle Bells
71.4
60.9
76.9
47.6
London Bridge
66.7
10.5
Mary Had a Little Lamb
100.0
66.7
100.0
53.3
Merry Christmas
100.0
100.0
88.9
88.9
Can-Can (Offenbach)
80.0
100.0
100.0
66.7
Row, Row, Row Your Boat
92.3
76.9
83.3
66.7
Scale Exercise
100.0
100.0
100.0
100.0
Silent Night
45.5
30.0
66.7
8.0
Surprise (Haydn)
83.7
88.2
77.3
85.7
Twinkle Twinkle Little Star
85.7
100.0
100.0
100.0
When the Saints Go Marching In
80.0
76.2
88.9
82.4
Hot Cross Buns
66.7
40.0
Itsy Bitsy Spider
66.7
100.0
66.7
50.0
Old McDonald Had Farm
57.1
66.7
80.0
100.0
Average
81.6
78.5
80.6
63.9
A.8 EXPLICIT-ALIGNEEN BASELINE
We adopt the same baseline introduced by Chou al. (2025). Their work provides an updated, open- open-source implementation of the methods by Benetos et al. and Wang et al., which remain the most directly relevant to score-informed error detection (Benetos et al. 2012 Wang al. 2017). While preserving the core principles of the original approaches, the re-implementation modify each stage of the transcription pipeline to align with recent progress in automatic music transcription (AMT). In particular, they replace the non-negative matrix factorization (NMF) transcription with the MT3 (Gardner et al. 2022) model, a state-of-the-an system. They also substitute Windowed Time Warping (WTW) with the more accurate Dynamic Time Warping (DTW). These updates yield comparable performance while extending support to multi-instrument settings.
A.9 ATTENTION PATTERN VISUALIZATION
We compare attention behaviors of three encoder designs: early fusion, late fusion (Polytune), and our proposed cross-attention alignment module (LadderSym).
A.9.1 POLYTUNE SELF-ATTENTION
Figure 6 visualizes per-layer attention maps from POLYTUNE. Diagonal patterns dominate the practice stream, while the practice stream exhibits vertical banding, indicating reduced temporal specificity. This asymmetry reflects that the practice stream encodes more global structure, while the practice stream retains local detail. The final layer also exhibits vertical banding, showing lack of locality in of the streams.
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Published as a conference paper at ICLR 2026
Plich
Layer
Layer
Layer 5
Layer
Time
Score
Stream
Pisch
Pitch
Time
Practice Siream
Pitch
Time
Pitch
Layer
Layer
Layer 10
Layer 11
Time
Layer (Joint)
Score Stream
High
Score
Pitch
Pitch
Time
Time
Practice
Practice Stream
Low
Pitch
Time
Figure 6: Per-layer attention maps in POLYTUNE. Maps are averaged over pitch. Layers 1-11 use independent encoders; layer 12 uses joint encoder. The score stream (top row) loses its local, diagonal attention structure in deeper layers. This demonstrates that this stream is sacrificing local information to build compressed, global representation of the entire clip. As tokens pass through deeper layers, the model can progressively "mix" with information from other tokens, thus causing the tokens to become less tied to their initial spatial location.
A.9.2 EARLY FUSION SELE-ATTENION (FULLY JOINT)
Layer
Lavera
Lasera
Layerd
Lower
Layer
Layer'
Taper
Layero
Layer
Lager11
Laper
Sieam
Score
Praciic
Score
Practice
Score
Pracice Steam
Score
Practice
Score
Practios
Score
Proctice
Streen
Stree
Figure 7: Self maps for the early fusion model. Each quadrant shows intra- inter-stream attention, averaged over the pitch axis. We observe strong alignment, but also strong locality in the intra-stream attention.
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Published as a conference paper at ICLR 2026
Figure shows quadrant attention maps from the FULLY JOINT early fusion encoder. Each quadrant represents one attention pattern: top-left is practice-to practice-to-practiee,, practice, bottom-right is practice-to to--tacticee, and the off-diagonal quadrants capture practice-to to-practice and practice-to cractice-ooppractic attention. All maps are averaged over the pitch axis to emphasize temporal alignment. This encoder exhibits strong diagonal structures, indicating that tokens attend mostly themselves nearby frames, preserving strong local correspondence in both streams.
A.9.3 LADDERS CROSS-ATTENTIOS
Figure 8 illustrates the cross-attention maps in LadderSym. These maps are averaged over pitch highlight temporal alignment. Unlike the previous models, LadderSym inserts cross-attention modules at each layer, enabling continuous alignment between the practice and practice streams. Earlier layers show more distinct diagonals, while later layers shift toward abstract correspondence. Moreover, we show that asymmetry is preserved via probing in Table one stream remains locally detailed while other emphasizes cross-stream integration. further investigated the first-layer
Y Aais Quary (Porformnns Spoctrorant
Spoctiog
Practice Stream
Tineo
Reference Score Stream
Aote Key (/lllrersss Spacting an)
Figure 8: Cross-attention maps in LadderSym, averaged over pitch. Axes represent token positions in practice and practice streams.
attention maps for specific error topologies to determine if interpretable geometric patterns, such as gaps for omissions or cross patterns for reversals, emerge. As shown Figure distinct these geometric variations are not observable in the first layer.
hypothesize that more abstract error-type reasoning occurs deeper layers. the case of skipped notes, the absence of a "gap" is likely because the model must actively attend to the corresponding silence the audio stream to verify the absence the expected pitch.
Similarly, the absence of cross-patterns in "flipped" sequences is result of our error taxonomy. We treat a permuted sequence not as distinct structural class, but as combination of insertion and deletion errors. For instance, if score sequence A - B C is performed C - B - A. the model resolves this locally as a "Missing C' Extra A" event the last, rather than actually detecting a reversal. Consequently, there is incentive to learn a cross pattern.
Extra C" event at the first position and a "Missing
A. INSTRUMENT-LEVE RESULTS
To expose the variation across instruments, we report per-instrument Error Detection F1 scores for LadderSym, Polytune, and the explicit-alignmen baseline.
We present instrument-level results of LadderSym versus prior work for every instrument in Table I0 We also provide qualitative examples in demo of the violin, piano, flute, and tenor sax.
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Published as a conference paper at ICLR 2026
P
P
"missed note"
"wrong note"
"flipped notes"
Figure First layer cross-altention maps in LadderSym, for various error scenarios. Axes represent token positions in practice and practice streams. Despite the differing error types, the attention patterns this initial layer exhibit highly similar, strong diagonal alignments, indicating that distinct signs of error detection are not yet observable at this stage and likely happen in later layers.
Table 10: Full results of error detection Fl scores for 14 instruments, split into Correct, Missed, and Extra note categories. We compare three models (LadderSym, Polytune, and a baseline). The row labeled "Average" summarizes all 14 instruments: piano from MAESTRO-E plus 13 additional instruments from CocoChorales-E LadderSym has better Fl scores across the board compared to other methods.
Correct (F1)
Missed (F1)
Extra (F1)
Instrument
Ours
Polytune Baseline
Ours
Polytune Baseline
Ours
Polytune Baseline
Average
97.4%
95.0%
37.0%
61.2%
49.2%
7.6%
63.2%
48.0%
25.9%
Piano
94.4%
90.1% 1%
43.5%
54.7%
26.8%
6.6%
86.4%
72.0%
39.9%
Flute
97.9%
96.0%
38.9%
68.7%
56.0%
7.2%
65.0%
52.0%
26.6%
Clarinet
97.8%
95.6%
38.3%
59.0%
49.7%
6.7%
61.0%
46.6%
24.1%
Oboe
98.0%
96.3%
33.4%
69.9%
58.4%
6.7%
62.6%
48.1%
25.9%
Bassoon
97.6%
94.4%
34.7%
62.2%
48.9%
6.4%
62.7%
41.7%
17.1%
Violin
97.6%
95.5%
36.1%
68.2%
57.1%
7.5%
62.9%
48.8%
27.3%
Viola
97.6%
95.1%
36.1% %
57.2%
46.9%
5.9%
59.9%
47.7%
26.1%
Cello
97.7% %
94.9%
37.5%
52.6%
42.7%
6.9%
61.4%
46.8%
21.7%
Trumpet
98.1% %
96.3%
37.8%
65.6%
58.7%
8.8%
65.3%
53.6%
26.6%
French Horn
97.8%
96.1%
38.4%
61.8%
53.9%
5.9%
57.1%
43.2%
23.7%
Tuba
97.7%
95.2%
37.3%
55.9%
45.4%
8.1%
64.8%
45.6%
17.8%
Trombone
96.8%
94.8%
35.0%
59.8%
50.4%
7.1%
58.7%
44.8%
21.7%
Contrabass
97.5%
94.2%
35.7%
54.9%
42.0%
8.9%
56.6%
38.6%
19.9%
Tenor Sax
98.6%
95.7%
39.7%
66.9%
56.2%
14.2%
60.4%
45.7%
25.1%
A.11 STATISTICAL ANALYSIS
To assess significance across the explicit-alignneee baseline, Polytune, and LadderSym, ran paired t-tests and Wilcoxon signed-rank tests on CocoChorales-EE and MAESTRO-E cnnferroni-correctte 0.017), and show the results in Table 11 Some of the computed p-values smaller than the smallest magnitude reliably distinguishable from zero in standard double precision (s 10-308), so we report them level.
10 300 Even at this threshold, all p-values remain far below our significance
A.12 SEED MANAGEMENT FOR REPRODUCIBILITY
To ensure reprodueibility. we implemented a consistent seed management strategy for model training. We utilized specific seeds for each stage ensure that results could be replicated exactly. Model Training: For model training, we used PyTorch Lightning's seed everything function with seed value of 365. This seed applied across all relevant components of the training process,
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Published as a conference paper at ICLR 2026
Table 11: Paired t-test and Wilcoxon signed-ran results.
Dataset
Comparison
t
Pt
W
Pw
Polytune vs. Explicit Align.
106.98
< 1
10
1.38 x 10
X 10 300
CocoChorales-E
Explicit Align. vs. LadderSym
-127.10
< x 10 300
8.75 x 10
x 10-300 300
Polytune vs, LadderSym
-21.85
4.98 x 10-103
1.79 x 10
1.47 x 10 170
Polytune Explicit Align.
86.18
< x 10-300
2.28 x 10
x 10-300 300
MAESTRO-E
Explicit Align. vs. LadderSym
-10.31
< 10 300
6.87 x 10a
< x 10 10-300
Polytune vs. LadderSym
-20.53
1.43 x 10 85
6.25 x 10
1.03 x 10
including data loading, model initialization, and training loops, ensure that training consistent and reproducible across different runs. The following code snippet (Listing T demonstrates how the seed was set for model training:
Listing I: Setting a seed with PyTorch Lightning's 's seed_eveng from pytorch_lightning import seed_everything
Set seed for model training
seed = 365
seed everyttin (seed)
A.13 LLM USE IN MANUSCRIPT PREPARATION
We used OpenAl's GPT-5 (via ChatGPT) to help with wording clarity and grammar-only edits. All scientific claims, experimental design, data analysis, and conclusions remain the responsibility the authors.
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Reviews:
[{"review_id": "Zw6eJLNX1x", "reviewer": "Reviewer_gi3B", "summary": "The paper introduces LadderSym, a multimodal Transformer architecture for music practice error detection, aiming to identify missed, extra, and wrong notes by comparing student practice audio to a reference score. LadderSym addresses these with two key innovations:\n\n1. A two-stream “Ladder” encoder that incorporates inter-stream alignment modules at every layer, enhancing fine-grained alignment between score and performance audio.\n\n2. A symbolic-score prompting strategy, where symbolic (MIDI) tokens are fed to the decoder, improving interpretability and reducing ambiguity.", "questions": "see weakness", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "• Proposes a novel interleaved encoder with layer-wise cross-attention modules, representing a meaningful architectural advancement over late-fusion approaches like Polytune.\n\n• Integrates symbolic prompts—a creative use of decoder conditioning that blends multimodal reasoning with interpretability.\n\n• The model is well-motivated and rigorously validated: strong baselines (Polytune, DTW-based), comprehensive ablations (fusion depth, input modality), and statistically meaningful improvements across datasets.\n\n• The paper is well-organized and accessible, explaining design intuitions (e.g., balancing local vs global feature specialization) with illustrative figures and tables.", "weaknesses": "• It would strengthen claims of generality to include instruments with continuous pitch (e.g., strings, voice) or evaluate robustness to stylistic variation.\n\n• The system’s inference latency (8.1 tokens/s) may hinder real-time feedback applications in tutoring contexts. A short section on runtime optimization or lightweight variants would make the work more actionable.", "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": "i5ivKTTWvi", "reviewer": "Reviewer_bM19", "summary": "The paper introduces LadderSym, a transformer-based model that improves error detection in music practice through two innovations:\n1)\tThe paper proposes a two-stream “Ladder” encoder that processes the practice audio and reference audio separately but aligns them at each layer using cross-attention (“inter-stream alignment modules”), which improves synchronization and comparison without forcing both streams to share parameters.\n2)\tSymbolic prompting, a prompting method that adds a symbolic (MIDI) score as a decoder prompt alongside the audio input, which reduces ambiguity caused by overlapping frequencies in the score’s audio.\nThese components together allow more accurate note-level classification of mistakes.", "questions": "1.\tThe authors mention “20 beginner performances” but do not specify: a) The number of unique players (only “three graduate students”) vs. performances (20 total — overlap unclear). b) The recording conditions (microphone type, room noise, instrument consistency). \n2.\tHow long are the symbolic prompts relative to the output sequence (do they truncate or pad)? Does the model ever ignore the prompt when symbolic and audio information disagree?\n3.\tWas any ablation done on partial or noisy symbolic prompts (realistic for imperfect MIDI scores)?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The system utilizes inter-stream alignment in every encoder layer, enabling the model to learn temporal dependencies similar to classical DTW but more robustly. The symbolic prompt adds contextual knowledge that helps with subtle or overlapping notes. Probing experiments show that each stream learns to specialize.", "weaknesses": "The model itself still struggles towards dense musical passages which might mask missed notes. Some errors near sequence boundaries may be mislabeled. The model is proposed under the assumption of roughly stable tempo, and extreme tempo deviations still require pre-alignment.", "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": "EDDfmbqIP1", "reviewer": "Reviewer_BK2b", "summary": "This paper introduces *LadderSym* for music error detection, featuring (1) a two-stream encoder with inter-stream alignment modules, and (2) the integration of both audio and symbolic scores as reference inputs. The model achieves state-of-the-art performance on both the *MAESTRO-E* and *CocoChorales-E* datasets.", "questions": "Could the authors please address the following:\n\n1. What does *globality* refer to, and why is coarse clip-level energy information used to measure it?\n2. Why is asymmetric feature extraction important for error detection? Intuitively, both practice and score streams should capture local information to localize and classify erroneous notes.\n3. Given the additional fusion steps, why does *LadderSym* have fewer parameters and faster inference speed compared to *Polytune*?\n4. In Figure 6, for the score stream, why are the attention maps concentrated at the beginning, especially in the last few layers (8–11), when the corresponding horizontal frames appear almost silent?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The improvement over the previous model (*Polytune*) is substantial.\n2. The proposed *Ladder* structure could potentially influence other tasks that require comparison between two sequences.\n3. The analysis of fusion stages provides useful insights for designing other multi-input models.\n4. Experiments conducted on a curated real-world dataset add value; if released publicly, this dataset could have a meaningful impact on piano error detection research.", "weaknesses": "1. The effect of incorporating symbolic scores is not sufficiently strong, as the relatively small gap shown in Table 4 weakens one of the paper’s two main contributions.\n2. The contribution of the *Ladder* design itself is questionable. Based on Tables 3 and 4, the position of the fusion stage appears to have a greater influence than the Ladder architecture, which fuses at every layer. On the *CocoChorales-E* dataset, simply moving from late fusion (*Polytune*) to the last three fusion layers improves the Extra-note F1 score from 46.8% to 59.6%. Introducing Ladder further raises it only slightly, to 62.3%. This suggests that the fusion location, rather than the Ladder mechanism, drives the improvement, making the claim that “cross-modal alignment should happen frequently” less convincing.\n3. The writing could be improved. Figures and tables are scattered throughout the text, and Section 3 (*Method*) is intertwined with experimental results. It is recommended to separate methodological and experimental sections more clearly for better readability.", "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": "gQedz55uhR", "reviewer": "Reviewer_3opm", "summary": "LadderSym introduces an interleaved two-stream transformer for music practice error detection.\nBy inserting cross-attention alignment modules and symbolic score prompts, it achieves substantial improvements over Polytune and classic DTW-based baselines. The model detects missed, extra, and wrong notes with high precision.", "questions": "Is the demo in real time? It looks like so from the demo page, but the method in the paper does not seem to support a real-time function yet.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Clear empirical improvement with solid ablation and baseline comparison. Music error detection is, in general, a challenging problem without elegant solutions so far. Very glad to see such a contribution.\n\n2. A fusion design that enhances alignment while preserving modality specialization.\n\n3. A connection with a larger picture: the model presented is, in spirit, well resonant with \"function alignment\"(https://arxiv.org/abs/2503.21106), a new theory of mind that is also closely related to music perception. I recommend citing and relating to it.", "weaknesses": "Possible improvements:\n\nThe paper could emphasize what is newly learned: attention visualization would clarify whether the model develops interpretable alignment behaviors.\n\nThe method could potentially apply to other music performance problems  (say, expressive performance).", "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)