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Semi-Automated Fingering Annotation

Updated 17 September 2025

The paper introduces a robust framework combining statistical HMM and BI-LSTM models to capture musical context and ergonomic constraints in fingering annotation.
It integrates multimodal approaches, including video-based pose estimation and GAN adaptation, to synchronize and refine finger-to-key assignments.
The framework extends to optimization and difficulty analysis for both piano and fretted instruments, supporting practical applications in performance and education.

Semi-automated fingering annotation algorithms are computational frameworks for assigning instrument fingerings—typically for piano or fretted string instruments—using statistical, deep learning, optimization, and hybrid schemes. They incorporate musical context, ergonomic constraints, and increasingly, multimodal data such as video or pose landmarks, to automate or assist the annotation process. These systems underpin advanced music information retrieval (MIR) tasks, performance analysis, and are integrated into educational and expressive modeling applications.

1. Statistical and Probabilistic Approaches

Statistical modeling is central to piano fingering annotation, exemplified by Hidden Markov Models (HMMs) and their higher-order extensions (Nakamura et al., 2019). The generative approach frames each sequence of finger assignments $f_1,\dots,f_N$ as latent states, with the corresponding note sequence $p_1,\dots,p_N$ as observations. For a first-order HMM: $P(f_1,\dots,f_N) = P(f_1)\prod_{n=2}^N P(f_n|f_{n-1})$

$P(p_1,\dots,p_N|f_1,\dots,f_N) = P(p_1|f_1)\prod_{n=2}^N P(p_n|p_{n-1},f_{n-1},f_n)$

To address the combinatorial explosion of parameters, transposition symmetry is invoked, so output probabilities depend only on relative pitch intervals: $P(p_n|p_{n-1},f_{n-1},f_n) = F(p_n-p_{n-1};f_{n-1},f_n)$ Higher-order HMMs model longer-range dependencies using pairwise output decompositions, with weighted coefficients for each prior note, and smoothed transitions via linear interpolation.

Chord-level HMMs extend this paradigm to polyphonic cases, modeling vertical (finger spread) and horizontal (chord-to-chord transition) costs, now defined as sums over pairwise costs (see Equations 6–7 in (Nakamura et al., 2019)). State inference is performed via Viterbi decoding to maximize joint likelihoods.

2. Deep Learning and Hybrid Models

Deep neural architectures—including feed-forward (FF), LSTM, and, crucially, bi-directional LSTM (BI-LSTM) networks—have been applied for fingering estimation (Nakamura et al., 2019, Zhao et al., 2021). The PdF model (Zhao et al., 2021) introduces pitch-difference (interval) as the input representation, processed through BI-LSTM layers to capture both preceding and succeeding context. This enables modeling of contextual dependencies that are difficult for traditional HMMs.

Finger transitions are enforced using learned transition matrices and prior finger-transfer rules, encoded as binary masks and recurrent update layers. For instance, physically impossible transitions (such as RH 2→3 descent without hand reposition) are pruned during inference. This is implemented as: $y(t) = (T \cdot W_T) \cdot y(t-1) + z(t)$ with $T$ being a decision mask based on prior ergonomic knowledge.

A new metric, incapable-performing fingering rate (IFR), quantifies the fraction of transitions that are physically unplayable. Empirically, BI-LSTM with pitch-difference input and finger-transfer constraints surpasses third-order HMMs in both general and highest match rates (+3%, +1.6%), and ensures zero IFR.

3. Multimodal Video-Based Annotation

Multimodal pipelines address the direct extraction of fingering from videos (Moryossef et al., 2023, Kim et al., 10 Sep 2025). These systems use synchronized video/MIDI data, pose estimation (often via deep convolutional pose machines or MediaPipe Hands), and GAN-based domain adaptation (CycleGAN), particularly to fine-tune pose models for out-of-domain, challenging video conditions.

Finger-to-key assignments are probabilistically estimated, typically by modeling the fit as a Gaussian over spatial proximity, normalized for multiple candidate fingers. Robust synchronization between video and MIDI streams is achieved by maximizing aggregate prediction confidence over temporal offsets.

A hybrid annotation strategy is applied for ambiguous cases in PianoVAM (Kim et al., 10 Sep 2025): automatic candidate selection based on landmark proximity scoring, followed by manual GUI review when the confidence falls below threshold (unique strong candidate required for automatic assignment).

4. Optimization and Stylistic Constraints in Fretted Instruments

Lead guitar fingering annotation algorithms solve multi-attribute optimization problems over position, string, hand spread, and expressive techniques (Bontempi et al., 12 Jul 2024). The cost function aggregates weighted terms for hand movement, discomfort, and timbral preferences: $\text{minimize} \sum_{i=1}^{n-1} \big[w_{PC} \cdot PC(i,i+1) + w_{SC} \cdot SC(i,i+1) + w_{HS} \cdot HS(i,i+1) + w_{TIM} \cdot TIM(i)\big]$ subject to biomechanical and timing constraints. Articulations (bend, vibrato, hammer-on, pull-off) are inserted using rule-based logic, informed by corpus-derived statistics (mySongBook) on stylistic technique frequencies. All outputs are formatted in MusicXML for downstream visualization and pedagogical integration.

5. Integration into Difficulty Analysis and MIR

Semi-automated annotation is pivotal for quantitative score difficulty classification (Ramoneda et al., 2022). Technique features—such as finger assignment matrices, finger velocities (from Pianoplayer [knowledge-driven]) and transition probabilities (from HMM [data-driven])—are used to construct high-dimensional feature matrices for machine learning models (XGBoost and GRU-attention).

Window-based feature segmentation (e.g., 9-note context) enables local and global difficulty prediction, with finger velocity and HMM-derived transition probabilities strongly correlating with expert-assigned difficulty ratings. The attention layer in GRU models provides interpretable local feedback, pinpointing technically demanding score regions.

6. Evaluation Metrics and Challenges

Evaluation utilizes multiple match rates (general, highest, soft, recombination), and increasingly ergonomics-aware metrics like IFR. State-of-the-art annotation algorithms (third-order HMM, BI-LSTM PdF) achieve general match rates >64%, with close human–human agreement ceilings at ~71% (Nakamura et al., 2019, Zhao et al., 2021).

Challenges include ambiguous hand landmark detection (motion blur, occlusion), 2D–3D projection complexity in video, score–video synchronization, and disambiguating multi-finger techniques. Hybrid correction interfaces (manual GUI review) remain necessary for resolving algorithmic uncertainties in both multimodal annotation and large-scale dataset construction (Kim et al., 10 Sep 2025).

7. Applications, Resources, and Future Directions

These algorithms support performance assistance, education, expressive modeling, and instrument-specific score generation (MusicXML for guitar, APFD datasets for piano). Public datasets (PIG, Mikrokosmos-difficulty, APFD, PianoVAM), released codebases, and online demos facilitate reproducibility and further research (Nakamura et al., 2019, Ramoneda et al., 2022, Moryossef et al., 2023, Kim et al., 10 Sep 2025).

Emerging directions include modeling higher-level musical context (phrasing, voice, inter-hand dependencies), integrating richer temporal and articulatory features, and refining network architectures (e.g., bidirectional recurrent models, GAN-based adaptation, transfer learning from large multimodal datasets). Cross-modal methods are expanding the scope to cover gesture analysis and performance practice research in other instrumental domains.

The semi-automated fingering annotation paradigm now spans probabilistic, deep learning, optimization, and multimodal frameworks—each with distinct modeling assumptions, technical challenges, and evaluation strategies. Their integration with modern MIR, education, and expressive performance modeling underscores their centrality in computational musicology and technology-driven pedagogy.