This project is part of CUHackIt 2022, and was produced as a collaborative effort by Grant Gonzalez, Dineshchandar Ravichandran, Owen Sullivan, Nikhil Suresh, and Zachary Ikpefua.
Purpose:
BeatFinder leverages Amazon's AWS platform (Lambda functions) and a host of open-source libraries (ffmpeg, etc.) to split audio from a selected portion of a YouTube video and identify background music using audio fingerprinting.
The project is comprised of a front-end, which parses URL input and allows users to select the start and end times for their clip, and a back-end which receives data from the user and processes audio. A haphazard mixture of home-grown scripts and publicly available functions are used to complete the analyzation, start to finish.
Contributions:
Each core function of the program and website were completed simultaneously by the various team members:
Grant Gonzalez — Research and learning about how to build the project.
Dineshchandar Ravichandran — Separation of foreground speech from background music using Python.
Owen Sullivan — Front-end in HTML, CSS, and JavaScript, as well as domain administration.
Nikhil Suresh — Separation of audio from YouTube video using Python, as well as Lambda function setup.
Zachary Ikpefua — Audio fingerprinting and identification of background music using Python.
Resources used:
StackOverflow — Used frequently throughout the project at various points of development. Some solution links are attached to the areas where they're applicable.
YouTube — Used frequently throughout the project at various points of development.
CUHackIT Mentors — Provided help with API calls throughout the course of development.