TrioAcustico

Inspiration

The inspiration for TrioAcustico came from the need to improve thunderstorm detection accuracy and provide better safety solutions for people in outdoor environments. Existing thunder detection systems often lacked real-time feedback and were inaccurate in determining the distance of the thunder. We wanted to create a system that could give instant, precise results by using sound data and triangulation, making it useful for both safety and scientific applications.

What it does

TrioAcustico is a thunder detection system that uses three or more microphones placed at fixed, predefined positions in a triangular formation. When a thunder sound occurs, the system captures the time it takes for the sound to reach each microphone. By analyzing these time differences, it accurately triangulates the origin of the thunder and calculates the distance to the strike. This data is then visualized through an intuitive real-time user interface, providing users with instant alerts and precise location-based storm information, enhancing situational awareness and safety during severe weather.

How we built it

We have set up a server-based architecture where three laptops equipped with microphones simulate the scenario of thunder detection. The system is designed to capture sound signals from a simulated thunderstrike (played through speakers) using microphones placed on each laptop. The data captured by the microphones is sent to the backend server, which performs triangulation based on the time differences between when the sound reaches each microphone. Using this data, the backend calculates the distance from the sound source. This information is then displayed in real-time on the UI, showing the calculated distance of the thunder sound.

Technologies Used: Flask: For the backend, handling data requests and logic for triangulation. Python: Used for the triangulation algorithm and data processing. JavaScript, HTML, CSS: For frontend development, managing real-time updates of the UI based on backend data.

Data Flow: A sound event is simulated by playing a thunder-like sound. The microphones on the laptops capture the sound at slightly different times. The captured data is sent to the Flask server. The server processes the timing data and calculates the location using triangulation algorithms. The UI updates dynamically to show the distance of the sound's origin. This system effectively simulates the real-world use of microphone arrays for detecting and locating sounds using triangulation. In the future, the setup will be expanded to incorporate actual microphone hardware and more advanced data processing methods.

Challenges we ran into

Microphone synchronization: Ensuring that the microphones are perfectly synchronized to capture the same sound at precisely the right time was challenging. We had to account for latency and differences in recording speeds. Triangulation accuracy: The accuracy of the triangulation is highly dependent on the microphone placement and the timing of sound detection. Fine-tuning the algorithm to ensure the results were reliable and consistent was a difficult task. Real-time processing: Ensuring that the system could process data in real-time without delays, especially with multiple sound events occurring at once, was a challenge. User Interface design: Designing a minimalistic yet informative UI that clearly displayed the detected thunder's position while also being user-friendly posed a challenge in balancing functionality and simplicity.

Accomplishments that we're proud of

Accurate triangulation: Despite the challenges, we succeeded in creating a system that accurately calculates the location of thunder with minimal error. Real-time alerting: The system provides real-time alerts that update the user with the thunder’s location and distance, ensuring they can react quickly. User Interface: We developed a clean, simple UI that clearly displays the detected information, making the system easy to use and understand, even for non-technical users. Successful integration: The system was successfully integrated with microphones, a backend server for processing, and a front-end interface that interacts smoothly with the data.

What we learned

Synchronization and precision are key: Fine-tuning the microphones and their synchronization for accurate triangulation was a critical learning point. We had to experiment with different configurations to find the optimal setup. Real-time data processing: Managing real-time data and ensuring that calculations are done quickly without any lags was a challenge but also a valuable learning experience in optimizing performance. UI/UX design in technical projects: While focusing on the technical side, we learned how important it is to make the end-user experience smooth and intuitive, especially when dealing with complex systems like triangulation and real-time data processing.

What's next for TrioAcustico

Integration with weather systems: We plan to integrate TrioAcustico with weather data sources to enhance its prediction accuracy and provide more comprehensive storm risk assessments. Deployment in outdoor events: We're aiming to deploy TrioAcustico in large outdoor venues like festivals and sports events to provide real-time thunderstorm alerts to attendees. Mobile app development: In the near future, TrioAcustico could evolve into a mobile application that pushes notifications directly to users’ phones, ensuring that thunderstorm risks are communicated effectively no matter where the user is. Scaling and refinement: As we gather more data and feedback, we will refine the triangulation model and test its scalability in various environmental conditions.