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MUSIC RESEARCH

 

client : BEING GOOD / wolfram research


expertise : UI / UX + music RESEARCH / composition + WRITING


deliverables : music analysis reports + music rating DATA + UX THEORIES

Being Good is a patented method for identifying and organizing moods in music content, using objectively measured scores for rhythm, texture and pitch (RTP) and clustered into six mood classifications based on an objective analysis of the measured scores. Digitized representations can be identified and organized based on the music's frequency data, three-dimensional shapes derived from the digitized representations, and colors derived from the frequency data.

I worked with a 3 member team (producer, programmer, legal) to develop the original concept of a mood-based classification system for music. My initial role was to research other music classification services (Pandora, Spotify, Apple Music, etc.), provide summaries of how the underlying technology worked, and eventually propose a theory of how mood could be as close to objectively derived from music utilizing the characteristics of rhythm, texture, pitch, and the 'audio fingerprint' of a song.

Once this idea was successfully patented , I became the lead music researcher. This consisted of selecting available music across a wide genre spectrum, listening to over 1200 songs, and writing my detailed analysis and report of the RTP elements for each.

https://patents.google.com/patent/US20150205864

 

Visualized data, showing intersections between the moods derived from music

 
 

Track I composed for a pitch analysis segment of the project

Visualized data showing more intersections of mood derived from music

Visualized data showing more intersections of mood derived from music

 
Frequency analysis of specific tracks to determine the overall 'pitch'

Frequency analysis of specific tracks to determine the overall 'pitch'

Spectrogram representation of individual instrument audio tracks

Spectrogram representation of individual instrument audio tracks