<aside> <img src="/icons/cursor-click_blue.svg" alt="/icons/cursor-click_blue.svg" width="40px" /> JukeBot revolutionizes music discovery, moving beyond words and filters to a deeply personalized journey through sound.
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<aside> <img src="/icons/color-palette_blue.svg" alt="/icons/color-palette_blue.svg" width="40px" /> Harnessing the unique mystery of chromesthesia, JukeBot transforms music listening by blending auditory and visual stimuli into a vibrant symphony of colors.
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Music enthusiasts often face a significant dilemma: the more they crave new sounds that tickle their ears and minds, the more they find themselves trapped in the echo chamber of their recommended playlists.
Meanwhile, newcomers to the music scene struggle to translate the tunes playing in their imagination into searchable terms, leaving them unable to explore genres and styles unfamiliar to them. JukeBot addresses these challenges, opening doors to a richer, more diverse musical universe.
Let’s get to the fundamental root of the problem. The first question is: why should we visualize music at all?
“Music is not just some data. It is art, it tells stories, and to consider it as just some numerical data doesn’t do justice to the ambiguity and mystery of why we are attracted to particular songs.”
So the next logical question is: how should we visualize music then?
https://www.youtube.com/watch?v=rLXYILcRoPQ
Figure 1: This scene from 'Ratatouille' vividly illustrates the subjective nature of sensory experiences, as each character uniquely perceives and interprets identical stimuli.
JukeBot's primary challenge lies in the subjective interpretation of mapping auditory stimuli to visual stimuli. Different individuals may perceive the same music genre or style with varying visual representations, such as colors or shapes. On the other hand, there are common verbal associations within auditory-visual mappings, such as associating sad songs with the color blue, which conveys sadness and low energy. This indicates a range of socially accepted mappings from auditory to visual stimuli. Identifying and leveraging this range is crucial for developing the algorithmic logic of JukeBot.
In today’s digital landscape, while music discovery platforms are abundant, they often fail to deliver personalized and meaningful experiences. Existing systems rely heavily on numerical data for recommendations, treating music more as data than as an art form, which strips away much of its context and emotional resonance. This conventional approach results in recommendations that feel impersonal and disconnected, neglecting the deep sensory and emotional bonds users have with music.