Music-Bike

Author: ethdiep

  • Week 9 Update – Testing On Bike

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    ethdiep
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    This week, we shifted focus toward real-world testing on the bike to evaluate how our adaptive audio responds during different ride conditions. The goal: determine what structure and sound design will work best for the final demo. ML Introduced a background InferenceService that runs machine learning on live sensor data (pitch, roll, yaw, g-force) using…

  • Week 7 Update – Preparing to Test

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    ethdiep
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    This week, we focused on enhancing both the functionality and polish of the adaptive music bike app. Here’s what we accomplished: ML FMOD Hardware/App Once the PCB arrives we can finally begin testing on the bike and train the ML model.

  • Week 6 Update – Tuning & FMOD Integration

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    ethdiep
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    This week, we made significant strides in both hardware tuning and software integration for the adaptive music bike system. The test version of the firmware was successfully updated to include three potentiometers, enabling real-time adjustment of the jump and drop detection thresholds. This allows for rapid fine-tuning during test rides without needing to reprogram the…

  • Week 5 Update – Hardware & ML

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    ethdiep
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    This week, we focused on both hardware stabilization to enhance sensor reliability and prepare for machine learning. We hope to begin testing on the bike soon. Hardware Improvements: Software Updates: Next Steps: