Cog 'n' Com

Inspiration

The idea for Cog ‘n’ Com came from moments when a great song would play, and we had no way of identifying it. While music recognition apps exist, few integrate personalized recommendations based on listening patterns. We wanted to create a tool that not only identifies songs instantly but also connects users to music they might love next.

What it does

Cog ‘n’ Com allows users to record a snippet of a song, instantly recognize it, and receive personalized music recommendations. By analyzing audio as well as user listening patterns, the app suggests new tracks tailored to individual tastes, making music discovery seamless and fun.

How we built it

Music Recognition and Recommendation:
We integrated with Gemini AI to study the provided music or inputs, then recommend songs based off of the analysis.
Frontend & Backend Integration:
The app features a responsive interface where users can record songs, view recognition results, and explore recommendations. The backend manages audio processing, API calls, and recommendation computations in real time.

Accomplishments that we're proud of

Successfully integrating a new product, Gemini AI with our code.

What we learned

How to extract meaningful audio features and implement machine learning for music recognition.
Techniques for building content-based and collaborative recommendation systems.
Best practices in integrating frontend, backend, and third-party APIs for a complete app experience.
Improved problem-solving and debugging skills.