EWNA 2026: Sensing Challenge

Project Name: “Sound Guardian” Date/Time: 9/23/2026 / 10:00 AM

Current Phase: Model Training & Fine Tuning

• Trained ML model based on the ESC-50 dataset, targeting glass shattering, falls/thuds, and familiar/unfamiliar voices. 
  • Gathered sample data for thud noises and falls, as well as voice recordings reading passages among our teammates for the familiar and unfamiliar voices.
• Added SMS Notification functionality
  • Successfully sends notifications to multiple numbers simultaneously

• Pivoted Back to RGB Matrix in order to have multiple matrices, one on-board, and one on light matrix, for outside and inside communication.
  • i.e. if someone falls, the light matrix is of no use to them, therefore sends a morse code sos signal to the other light to signal outsiders.

Project Name: “Sound Guardian” Date/Time: 9/22/2026 / 6:00 PM

Current Phase: Prototyping

Scrapped 8x8 LED Matrix for 8x13 LED Matrix present on Arduino Uno Q Board. Left us with no option but to draw certain images to differentiate between threats. Image display is completely implemented, with different functions to display different images, such as glass breaking or a fire alarm going off.

Fully implemented SMS messaging based off of amplitude of audio detection as proof of concept. Pivoted towards ML but still have not completely trained the model to use for threat detection. Found a dataset to train ML called ESC-50 to train model on, as well as our voices to get "familiar voices" and "unfamiliar voices" detected.

Arming/disarming placed on the backburner to focus on main problems with ML and audio detection.

The Mac processes audio from the USB microphone and sends the data over USB serial to the UNO Q. A Python relay running directly on the UNO Q's Linux side reads that serial data and forwards it over TCP into the App Lab container. This lets App Lab receive the microphone data for ML classification and then trigger alerts and LED responses.

Project Name: “Sound Guardian”

Date/Time: 9/22/2026 / 1:00 PM

Current Phase: Brainstorming

Started brainstorming ideas for what direction we wanted to steer our audio classification/threat detection project.

• Use given RGB LEDs on the 8x8 matrix to show how severe a potential threat may be
  • Fading Green/Blue for safe environment/idle
  • Pulsing Red for danger when detected
  • Use 8x8 matrix to draw out certain images when detecting specific threats
• Use SMS messaging to send a phone alert to whoever has connected to the device
  • Logs time and detected threat
  • Instant potential response
• Two options for audio detection
  • Use frequency filtering and harmonics to detect certain types of sound
  • Train ML model to detect sounds that signal potential threats
• Add (potentially remote) arming/disarming so that sound detection isn’t constantly running while audio such as the TV or guests are in the room the device is