Winner of Most Creative Use of Nasdaq Quandl Datasets, HackMIT 2020
We created an auditory data visualization tool that uses differently-pitched tones to indicate various tresnds within the given data. We aimed to leverage the human brain's ability to discern several different tones playing at once, even if they are playing simultaneously.
The application takes an input csv dataframe and normalizes each of the series. Each value is then mapped to a pitch, and the pitches are compiled together into a MIDI file. The logic was written in Python using the midiutil library. The code also has the capability of varying volume instead of pitch.
We also created a web interface for our application, using Flask to connect our Python music functions to an HTML/CSS frontend without needing any Javascript.
This project won the "Most Creative Use of Nasdaq Quandl Dataset" sponsor award, given by Nasdaq, at HackMIT 2020.
For more information, check out our GitHub repository!
