This study explores how micro-changes in the plucking trajectory of a guitar pick influence the sound of an acoustic guitar. Using a state-of-the-art robotic plucker, a series of measurements has been performed, where the plectrum was moved towards the instrument by a step of 192 micrometers, resulting in an increased attack depth. It has been analysed how the effect of these changes is reflected in loudness, timbre, harmonic content and how the sound progresses during decay. This methodology has been repeated for guitar plectra made from six different materials to investigate how the pick itself influences the effect of a change in the plucking trajectory. The results of the study show that at a low depth the string is not fully excited resulting in weak and markedly altered sound. The range of this effect changes with the mechanical properties of the plectrum material. After this range an increase in depth results in an increase in sound loudness, a decrease in inharmonicity and noisiness and a shift in timbre where the sound becomes fuller in low frequencies and rougher. Presented findings help to understand the nuanced relationship between plucking trajectory and acoustic output. They provide important insights regarding the importance of plucking in guitar testing methodologies, showing that the mech
Recent advances in humanoid robotics and embodied intelligence have enabled robots to perform increasingly complex manipulation tasks. However, musical instrument performance remains a formidable benchmark, demanding not only collision-free trajectory execution but also precise contact timing, asymmetric bimanual coordination, and target acoustic outcomes on physical instruments. The guqin, a seven-string fretless zither, presents unique manipulation challenges due to its millimetric string spacing, transient right-hand plucking, and sustained left-hand harmonic contacts. In this work, we present a physical heterogeneous dual-arm robotic system for phrase-level autonomous guqin performance. We formulate guqin playing as a hybrid discrete--continuous execution problem and develop a hierarchical planning framework that coordinates working finger assignment, configuration continuity, obstacle avoidance, and tight bimanual contact schedules across consecutive musical events. The system integrates vision-guided instrument localization, tactile-based harmonic contact monitoring, and auditory feedback-informed plucking parameter calibration. Real-world experiments on a 25-event phrase demonstrate that the system reliably executes coordinated open-string and seventh-hui harmonic sequences on a physical guqin, achieving 93.6% and 96.8% event correctness across repeated trials.
Identifying which string produces a given pitch in monophonic electric guitar audio is a classification challenge: a single pitch can often be produced on multiple strings, with timbral differences largely imperceptible to untrained humans. We present Fretiq, a preliminary single-instrument, single-player browser-based string classification system using a 26-dimensional feature representation of frequency band energies, spectral statistics, and 13 Mel-Frequency Cepstral Coefficients. Across five seeds, a shuffled frame-level validation split yields 97.25 +/- 0.32 percent accuracy, with an ablation study identifying MFCCs as the primary accuracy driver (92.09 +/- 0.50 percent without MFCCs). We introduce Comparison Training, a data collection method recording same-pitch pairs on adjacent strings in deliberate alternation. An initial shuffled-split comparison found no net benefit but was confounded by non-comparable validation sets across conditions. A corrected matched evaluation, using an identical recording session held out from training and model selection, shows including comparison-session data improves accuracy by 25.78 +/- 1.46 percentage points; a size-matched control shows this is not explained by training-set size alone. A recording-session-held-out evaluation yields 86.53 +/- 1.23 percent accuracy, closely matching an independently collected free-play evaluation (87.8 percent), both well below the shuffled-split figure, showing shuffled validation substantially overestimates real generalization here. We describe the feature extraction pipeline in Python and TypeScript for training-inference parity and document two implementation failure modes. The system runs entirely in-browser with no specialized hardware required.
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.