Abstract
Current physiological audio‑feedback systems may rely on reactive media adjustments (e.g., track skipping, uniform time‑stretching), which can disrupt user immersion or introduce acoustic phase distortion. This publication describes a predictive audio modulation system that dynamically isolates and modulates individual instrumentation layers within an active audio track. The system utilizes a machine learning model to anticipate pacing deviations based on cardiovascular exertion and topographical data. To improve synchronization, the system phase‑locks audio transients to biomechanical footstrikes using an inertial measurement unit (IMU). Further, the system employs an asymmetric computing architecture to dynamically balance computational loads between a primary wearable device and a paired mobile handset device to manage local thermal constraints. This approach provides proactive audio pacing without requiring abrupt media switching or uniform pitch distortion.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Agarwal, Nikita, "Phase‑Aligned Predictive Audio Modulation Utilizing Biomechanical Telemetry and Asymmetric Compute", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10286