The original brief I set myself was to create a music recommendation system that was fully visualised. I envisaged a set of visual symbols that would be created by the very music that they represented through a generative visual system taking in various aspects of data from it. You would then be able to get an intuitive, immediate overview of different pieces of music and compare or relate those to others.
In contrast to the majority of recommendation systems that base their recommendations on dominant listening trends and patterns, (such as Amazon and Spotify) this system would be rooted in the actual sound properties of a piece of music and so would be closer to Pandora, though less comprehensive (Pandora aggregates 400 characteristics for each song) and more indicative.
However, at the point where I started to actually break music down into data and translate this into visuals, I realised just how reductive this process was and, crucially, how no amount of quantitative data (not even 400 characteristics per song) would be able to define an atmosphere, sentiment or meaningfully pre-empt the subjective, esoteric intangible that is personal taste. As a friend summarised neatly in a conversation about my idea, we don't like music because it has X beats per minute- "its just a whim".
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