We wanted to create a more efficient way to discover music, that saves time. We were also intrigued by the math behind music discovery algorithms.
What it does
Given a series of images, the user selects images that best match their current mood. Our algorithm will determine the approximate mood of the user and will return a list of songs that they may enjoy accordingly.
How I built it
We learned and utilized web development technologies to make this application possible. For the front end, we used the bootstrap framework to build an intuitive and attractive user interface. For the backend, we used Node.js and Express.js to implement our music discovery algorithm. And we used Microsoft's Azure platform to host our application in the cloud. The Azure services we used were Windows VM, SQL database, and the cloud app platform hosts our entire program.
Challenges I ran into
Accomplishments that I'm proud of
Like we mentioned above, we learned a lot of information and the end result was a cool application.
What I learned
What's next for SongInstinct
We plan to refine our algorithm and make our user interface more streamlined so that we can more accurately predict music recommendations based on a users mood-based responses to different images. We also want to plug our app into the Spotify API, so that the user can listen to the music recommendations in our site, using Spotify, without necessarily having to leave SongInstinct