TART and the Physics of Guitar Transcription

TART is a modular framework built to generate guitar tablature with fingering and expressive technique annotations directly from raw audio. It replaces monolithic models that previously struggled to map pitches to specific string-fret locations, often producing technically impossible tab. By separating pitch detection from fretboard mechanics, TART moves away from end-to-end black-box systems, instead treating physical constraints as explicit inputs to the generation process.

Previous systems treated guitar transcription as a direct mapping problem from audio to MIDI. This approach failed because a single pitch can exist in multiple locations on a standard guitar fretboard. Models that ignored the physical layout of the neck frequently produced impossible fingerings. Because earlier systems were trained on pristine studio recordings, they also lacked the robustness to filter the percussive noise and resonance characteristic of real-world playing. TART addresses this through a four-stage pipeline: raw audio is converted to MIDI, analyzed by a technique classifier, resolved by a T5 encoder-decoder that considers conditional audio features, and finally formatted into standard tablature.

For developers, the separation of string-fret assignment from initial MIDI transcription is the key mechanism. The T5 encoder-decoder acts as a conditional logic layer. It receives the predicted MIDI notes alongside output from the technique classifier, which identifies labels like slides or bends. This label is transformed into a vector token that is prepended to the T5 input sequence. During the autoregressive decoding process, the T5 model uses self-attention to correlate these technique tokens with the latent audio embeddings. The tokens act as global constraints on the decoder's hidden states, biasing the output logits toward valid fretboard configurations and effectively filtering out sequences that violate the physical feasibility of the detected technique.

Consider the mechanics of a whole-step bend on the second string at the fifth fret. A standard model might see the pitch and blindly map it to the seventh fret of the same string. TART identifies the bend via its technique classifier. By including the bend token as a conditional input, the decoder is forced to evaluate the feasibility of the movement relative to the fret position. The model shifts its probability mass toward the fifth-fret position because it has learned that this specific configuration accommodates the mechanical requirements of a bend—specifically the string tension and the necessary travel distance for the pitch shift. This ensures the output reflects the physical realities of the instrument rather than simple frequency matching.

Latent audio embeddings carry the information needed to resolve the ambiguity of guitar performance. These embeddings capture high-level features derived from the input waveform, including harmonic content and spectral decay. When the model assigns a string, it compares the current latent representation of the audio against the learned acoustic signatures associated with different fretboard positions. For example, a note played on a wound string versus an unwound string will exhibit different partials and higher-frequency energy decay. By comparing the latent signature of the input audio to the learned features of different strings, the model performs a search within its latent space to select the string-fret combination that minimizes the distance between the acoustic signature of the recording and the expected resonance of a particular physical position.

The system reaches a 54.08% end-to-end Tab F1 score when evaluated as an average across four datasets: GuitarSet, the Electric Guitar Dataset (EGDB), and their respective noisy variants. We do not yet know how the system behaves with non-standard tunings or instruments with significant intonation issues, as those variables are often smoothed over in clean training sets. If the modular approach relies heavily on specific latent audio embeddings, it may struggle with heavily distorted electric signals compared to clean acoustic takes. The primary unknown is whether the latent embeddings maintain their string-discriminating power when the audio signal is saturated by overdrive or heavy compression. In these cases, clipping and gain-induced saturation likely compress the dynamic range of the signal, potentially washing out the subtle timbral cues the model requires to correctly map pitch to a physical string location.