SearcharxivSearch

arXiv · 2604.21682

PHOTON: Non-Invasive Optical Tracking of Key-Lever Motion in Historical Keyboard Instruments

Abstract

This paper introduces PHOTON (PHysical Optical Tracking of Notes), a non-invasive optical sensing system for measuring key-lever motion in historical keyboard instruments. PHOTON tracks the vertical displacement of the key lever itself, capturing motion shaped by both performer input and the instrument's mechanically imposed, time-varying load. Reflective optical sensors mounted beneath the distal end of each lever provide continuous displacement, timing, and articulation data without interfering with the action. Unlike existing optical systems designed for modern pianos, PHOTON accommodates the diverse geometries, limited clearances, and non-standard layouts of harpsichords, clavichords, and early fortepianos. Its modular, low-profile architecture enables high-resolution, low-latency sensing across multiple manuals and variable key counts. Beyond performance capture, PHOTON provides real-time MIDI output and supports empirical study of expressive gesture, human-instrument interaction, and the construction of instrument-specific MIDI corpora using real historical mechanisms. The complete system is released as open-source hardware and software, from schematics and PCB layouts developed in KiCad to firmware written in CircuitPython, lowering the barrier to adoption, replication, and extension.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Noah Jaffe, John Ashley Burgoyne. 2026-04-23. PHOTON: Non-Invasive Optical Tracking of Key-Lever Motion in Historical Keyboard Instruments. https://arxiv.org/abs/2604.21682

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Diarization Error Decomposition Under Pause Annotation Ambiguity

Speaker diarization evaluation is sensitive to ambiguity in pause annotation, which can inflate diarization error rate (DER) or obscure genuine model errors. We show that morphological closing, which has been used for pause-tolerant diarization evaluation, discards segment-level distinctions. Instead, we propose an exact, overlap-aware decomposition of standard DER into a pause-attributable component, consisting of errors compatible with pause filling, and a residual core component that can serve as a proxy for intrinsic diarization errors. The decomposition leaves DER unchanged, while the pause-attributable and core components vary monotonically with the pause threshold and eventually saturate. Experiments spanning synthetic transformations, annotation mismatch, cross-domain evaluation, and tight-boundary diarization show that the decomposition reveals error sources not apparent from standard DER.

eess.AS

Less can be More: What Aspects of Speech Drive End-of-Turn Detection

In conversational AI, detecting when a speaker has finished talking is crucial for natural turn taking. While recent work incorporates semantics, the relative contribution of different modalities remains unclear. We present a controlled ablation of acoustic, prosodic, and semantic signals for streaming end of turn detection using a lightweight trimodal classifier. Under identical training conditions, the acoustic prosodic combination achieves the best balance of accuracy and latency, achieving utterance F1 of 0.93 with 7.8% false alarms at 400ms median latency. Adding text increases premature detections without improving performance. Feature space analysis confirms that prosodic features have the strongest class separability, while text representations overlap substantially. These findings suggest that turn-taking is primarily conveyed through intonation and silence patterns rather than semantic completeness, enabling faster and more reliable systems without expensive text inference.

eess.AS

Downstream-Task-Aware Unified Source Separation

Task-aware unified source separation (TUSS) enables a single model to handle diverse separation tasks by conditioning on input prompts. However, conventional TUSS does not account for downstream task requirements, such as whether the enhanced speech will be used for human listening or automatic speech recognition (ASR). In this paper, we propose a prompt extension framework for TUSS that incorporates downstream task information into the input prompts and switches the loss function according to the given prompt during training, enabling outputs with different signal characteristics at inference time. Specifically, we introduce an ASR-dedicated prompt paired with a regularized loss function that reduces speech artifacts to improve ASR robustness, while the standard prompt is paired with the conventional SNR loss function. Experiments on the LibriSpeech and JNAS corpora demonstrate that the proposed joint-training scheme enables a single model to improve ASR performance over noisy input across a wide range of SNR conditions by selecting the ASR-dedicated prompt, while maintaining general speech enhancement quality when the standard prompt is used.

eess.AS