Source-linked AI summary
Playability-Aware Audio-to-Tablature Guitar Transcription via Diffusion Models
Riccardo Simionato, Louis Bigo
TL;DR
Noise2Fret addresses guitar tablature transcription as both pitch detection and string-fret assignment, incorporating musical and physical constraints into a diffusion-based generative model. It outperforms baselines across evaluated datasets and metrics while using fewer floating-point operations, and auxiliary losses provide consistent gains.
Problem
Guitar tablature transcription must resolve string-fret positions in addition to pitch, but existing approaches treat it as standard classification without modeling musical and physical playability constraints.
Method
Noise2Fret uses a diffusion-based end-to-end framework with continuous representations of discrete tablature targets, conditioned on audio features and trained with five auxiliary losses encoding musical and physical constraints.
Results
Noise2Fret outperforms TabCNN and FretNet across evaluated F-measure metrics on GuitarSet and GOAT, while requiring 341M operations per forward pass versus 3.4B and 17.4B for the baselines.
Takeaways & Limitations
Combining auxiliary losses with complementary audio conditioning improves transcription performance while retaining a favourable computational profile for practical deployment.
Takeaways & Limitations
Experiments are limited to standard-tuned recordings without effects processing, excluding non-standard tunings, electric timbres, and capo-containing examples.
Abstract
from arXiv · showhide
Guitar tablature transcription requires not only accurate pitch detection but also assigning each note to a specific string-fret position, as the same pitch can be played at multiple fretboard positions. Existing approaches treat this as a standard classification problem, ignoring the musical and physical constraints that govern playable fingering sequences. We propose Noise2Fret, a diffusion model for audio-to-tablature transcription that generates tablature through a continuous latent representation of discrete fret and string targets, conditioned on spectral and audio features. To bridge the gap between pitch accuracy and physical playability, we introduce five auxiliary losses encoding Pitch-Class Distance, Positional Distance, Circle-of-Fifths Distance, String Similarity, and Hand-Span Feasibility directly into the training objective. Experiments on GuitarSet and GOAT datasets demonstrate that the model outperforms baselines while remaining computationally more efficient, and that the auxiliary losses yield consistent gains over the standard training objective.
1 Related Work
Guitar transcription has progressed from traditional signal processing to end-to-end neural systems, while recent work increasingly incorporates guitar-specific fingering and playability constraints. These approaches motivate task-specific objectives and representations for mapping audio to tablature.
- Traditional guitar transcription jointly estimated note timing and pitch with string number, plucking style, and expressive techniques.
- Dynamic programming was used to model physically feasible fingering configurations in guitar transcription.
- TabCNN pioneered end-to-end audio-to-tablature mapping with CNNs, with spectral-temporal features implicitly encoding playability constraints.
- Later systems added self-attention, beat-informed quantization, synthesized data, and pairwise likelihoods for co-occurring string-fret combinations.
- EGDB and GAPS broadened guitar transcription datasets and benchmarks, while domain adaptation from piano transcription produced strong zero-shot GuitarSet results.
2 Data representation
The representation encodes guitar performances as symbolic sequences of string-fret assignments, using per-string class labels and continuous embeddings alongside spectral and temporal audio features. These representations preserve fingering information while supplying pitch and onset cues for transcription.
- Guitar tablature represents performances as sequences of fret and string assignments, including physical fingering positions and playing techniques.
- Guitar Pro and JAMS provide note-level string, fret, timing, and technique annotations that standard MIDI cannot supply.
- Each event contains one or several simultaneous strings and is represented by six per-string class labels.
- Each per-string label encodes muted, open, or fretted states through F = Mf + 2 classes, where Mf is the dataset’s maximum fret.
- Discrete fret classes are mapped through learned embeddings and concatenated into a continuous per-event representation.
- The audio features include STFT magnitude, spectral flux, and brightness extracted from 16 kHz mono waveforms.
- Spectral flux supplies onset-strength cues, while brightness helps distinguish physically equivalent pitches across string-fret positions.
3 Methodology
Noise2Fret treats tablature transcription as continuous-space diffusion conditioned on audio and spectral features, then decodes per-string fret classes. Its training objective adds differentiable losses for pitch, harmony, position, string selection, and hand-span feasibility.
- Architecture: A 1D convolutional U-Net with ResNet blocks, skip connections, and a self-attention bottleneck maps noisy event sequences back to per-string fret logits.At inference, deterministic DDIM sampling iteratively denoises the representation before selecting fret classes.
- Diffusion Framework: Noise2Fret embeds tablature, adds Gaussian noise, and learns iterative denoising in a continuous representation using a v-prediction diffusion framework.The model predicts velocity rather than noise directly, enabling recovery of a clean tablature embedding.
- Conditioning: Audio features condition denoising toward tablature consistent with the recording, while spectral features capture pitch, temporal boundaries, and playing position.Raw audio, STFT, spectral flux, brightness, and diffusion timestep are injected throughout the network.
- Musical Auxiliary Losses: Five auxiliary losses encode pitch-class agreement, harmonic distance, physical fret position, active-string overlap, and hand-span feasibility.The losses are designed to operate on soft predictions, preserving differentiability during training.
- Musical Auxiliary Losses: Pitch-class and Circle-of-Fifths losses distinguish pitch mismatches from their harmonic distance, while positional and string losses penalize incorrect physical assignments.Pitch classes are represented as 12-dimensional vectors; positional distance excludes open strings and muted slots, and string similarity uses Jaccard distance.
- Musical Auxiliary Losses: Hand Span Feasibility loss is zero for playable configurations and penalizes only excess fret separation beyond the maximum feasible span.It uses the maximum and minimum fret positions among concurrently fretted, non-open strings.
4 Experiments
Experiments use GuitarSet and GOAT under defined recording and preprocessing constraints, train Noise2Fret with diffusion and auxiliary objectives, and systematically ablate design choices.
- 4.1 Datasets: GuitarSet provides approximately 3 hours of annotated acoustic guitar recordings, while GOAT provides 5.9 hours of real recordings with fret/string annotations.GuitarSet uses six-fold cross-validation; GOAT targets full tablature windows aligned to annotated onsets.
- 4.2 Training Details: The diffusion process operates in continuous embedding space, combining embedding-space MSE with cross-entropy rounding loss for discrete tablature prediction.Logits are recovered by projecting predicted embeddings against learned class vectors, with the rounding loss weighted at λ = 0.1.
- 4.2 Training Details: The total objective combines the base diffusion loss with auxiliary positional, pitch-class, Circle-of-Fifths, string, and hand-span losses.The auxiliary weights are calibrated to maintain comparable loss magnitudes across terms, with hand-span loss disabled for GuitarSet because it contains capo performances.
- 4.2 Training Details: Models are trained with AdamW for up to 1000 epochs using cosine-annealed learning rates, batch size 128, and a six-fret hand-span threshold.The base channel width is 64 and the initial learning rate is 3 × 10^-4.
- 4.3 Ablation Study: A systematic ablation study removes auxiliary losses and conditioning signals individually to isolate each design choice.The two ablation axes are auxiliary loss functions and conditioning signals.
5 Results
Noise2Fret outperforms the compared tablature baselines across reported F-measure metrics on GuitarSet and across metrics on GOAT, while auxiliary losses provide consistent gains and conditioning affects performance differentially.
- GuitarSet Results: Noise2Fret outperforms TabCNN and FretNet across all F-measure metrics on GuitarSet, with auxiliary losses slightly improving performance across all metrics.The result is reported for the proposed 64-dimensional diffusion-based sequence model.
- Metric Analysis: Pitch precision is marginally lower than baselines, but recall improves substantially; near-perfect TDR indicates reliable string-fret disambiguation after correct pitch detection.The reported trade-off concerns pitch detection, whereas TDR concerns resolving string-fret ambiguity.
- GOAT Results: Lower absolute metrics on GOAT reflect its greater difficulty, while Noise2Fret performs better across all reported metrics than adapted baselines.The baselines were re-implemented and adapted to GOAT, which may slightly affect their performance.
- Auxiliary-Loss Ablation: The combined auxiliary loss achieves consistent gains across almost all metrics while maintaining competitive recall, resolving individual losses’ differing precision–recall trade-offs when jointly optimized.Lpos has the highest recall and lowest FNR, whereas Lpc preserves high precision and low FPR at the expense of recall.
- Conditioning Ablation: The full conditioning configuration performs best across all metrics; removing STFT causes the largest degradation, while other removals affect precision or recall differently.Removing raw audio lowers recall and increases FNR, while spectral flux affects recall more strongly and brightness produces a modest precision drop.
- Efficiency: Noise2Fret requires 341M operations per forward pass versus 3.4B for TabCNN and 17.4B for FretNet, achieving a 0.67 real-time factor on an RTX 3090.The model has 15M trainable parameters, more than both baselines.
- Qualitative Example: A GOAT test example compares ground-truth and predicted tablature, marking incorrect string-fret assignments and adding reference Let Ring annotations absent from the ground truth.The annotations are shown for reference rather than as part of the ground-truth tablature.
6 Conclusion
Noise2Fret jointly performs pitch detection and string-fret assignment through an end-to-end diffusion framework, outperforming TabCNN and FretNet while benefiting from combined auxiliary losses. Future work targets broader technique and tuning coverage, real-time optimization, annotation gaps, and longer audio context.
- Noise2Fret jointly addresses pitch detection and string-fret assignment in an end-to-end generative framework.
- Noise2Fret achieves superior F-measure performance to TabCNN and FretNet.
- The combination of five auxiliary losses yields broadly improved performance across evaluated metrics despite individual precision–recall trade-offs.
- Audio features provide useful complementary information according to the conditioning ablation, while the system maintains a favourable computational profile.
- Future work includes playing techniques, non-standard tunings, real-time architectural optimization, GOAT annotation gaps, and longer audio input windows.The paper specifically identifies missing Let Ring labels in GOAT and preliminary evidence that longer input windows may improve accuracy.
7 Ethics Statement
The system is presented as a transcription aid for musicians, educators, and researchers using publicly available GuitarSet and GOAT research datasets under academic-use licenses. It is not intended to generate new musical content.
- Noise2Fret is designed to assist musicians, educators, and researchers with music analysis and learning.
- The study uses publicly available GOAT and GuitarSet datasets released under appropriate licenses for academic use.
- The system is intended as a transcription aid rather than a generative tool and does not produce new musical content.