Team’s Status Report for 3/14

This week our team made progress on both the software and hardware components of the project. On the software side, significant time was spent debugging the build infrastructure to successfully integrate Omnizart, the machine learning model we plan to use for note identification, into our existing system. After resolving several complex build issues, Omnizart was successfully compiled, and work has begun on transitioning our current note identification pipeline to use this model, though the full pipeline is not operational yet. We also improved the user experience for the learning platform by creating a home page and a page that allows users to upload songs to be parsed, which also begins to support future hardware integration. On the hardware side, the drum pad system has been successfully linked to the transmitter and is now able to send hit intensity levels to the central hub. Sensitivity calibration is currently being performed, and the voltage threshold will likely be increased from 50 mV to around 80 mV to prevent noise from being interpreted as hits. Work has also begun on implementing the Multiceiver functionality of the Nordic nRF24L01+ to allow the receiver to handle messages from multiple pads simultaneously, as well as initial development of the DSP pipeline so audio feedback can be heard by the user. While some hardware work is slightly behind schedule, additional lab time this week should allow us to complete calibration, measure end-to-end latency, and begin integrating the hardware and software systems together.