beatRoot

beatRoot logo
ex. output w/ spotify connection
UI
Song Analysis for recommendations (Spectrogram)
Song Analysis for recommendations (MFCC)

Inspiration

We wanted to create a DJ that can recommend songs based on the 'feel' of a song but is curated to support an activity you want to do like studying, walking, meditation, etc. During the activity, we wanted the DJ to measure our emotional/mental state and recommend songs accordingly.

What it does

The user will enter a song they like and a target activity. Then, beatRoot uses the heart rate sensor to get an understanding of physical/mental state and recommend songs based on the reference song and target activity. The goal of beatRoot is to create a constantly evolving playlist that will help maintain an optimal heart rate (thereby mental state). Similar to the way the sympathetic and parasympathetic nervous systems will counteract each other to achieve homeostasis, we dynamically adjust the parameters for playlist creation to smoothly transition you from your current state to your desired state. This dynamically created playlist can directly play through a connected device on Spotify at all times.

How we built it

We curated multiple song profiles to suit each activity in order to target heart rate, tempo, energy, etc. using Spotify API. We used an Arduino Uno with a pulse sensor to get heart rate readings. The song recommendation was assisted by our CNN-based song spectrogram feature extraction methodology.

Challenges we ran into

We ran into challenges in identifying song features that were used to extract the 'feel' of a song. We had to run several song analysis sessions to identify some tractable features to solve the problem. We also experienced unexpected Spotify web API downtimes.

Accomplishments that we're proud of

We are proud of the novel approach of song discovery we created.

What we learned

This was our first hardware project and we learned a lot about using Arduino and sensors. We also gained a vast amount of knowledge on the technical aspects/components of what audio and music.