Haddow Press Experiment

DJ Modular Remix Engine - Part One

This is the first part in a series of articles I’m putting together to sketch out some ideas for a fully automated electronic music synthesizer-tool. In the long term I’d like to do a little more patent research and move towards finding some venture capital to actually build the thing. But for now, I’m sharing the concept because (quite simply) I’m more interested in OWNING one than I am in being the MARKETING agent for such a product. If you want to build it, please…

The Background

Recorded music has freed us from live performance. While portable players have freed us from bulky home stereos. Music compression (MP3, iPod, Zune, etc.) has freed us from the obstacle of large physical storage limitations. What the problem is now is content and the inability for music itself to be freed from the process of creation. Electronic music has taken us a step in the right direction, but rather than freeing us from static songs and creating a new world of dynamic music creation, we’ve been limited to pre-mixed, pre-recorded samples. This has built the power behind an amazing new genre over the past two decades, but until we FREE ourselves from this final barrier, we will forever be trapped by the very nature of recorded music itself: a fragment of sound recorded somewhere else and only vaguely related to our current experiences.

What I would like to create is a portable music player that performs a number of tasks simultaneously:

1. REMIX: Live, continuous remix of samples, sounds, beats, tones, chords, and recorded noise into a dynamic, continuously adapting sound.
2. MODULAR INPUT: The player understand the environment through a number of input devices that measure the world and the responses of the user to build and adapt the music to the present situation. This could include light, heat, sounds (that affect but also incorporate back into the song through recording tools), a GPS to track relative speed and velocity of the user, gyroscopic sensors that can detect smaller movements such as dancing, running, or other actions, and other similar modules that influence the final sound.
3. LEARNING: Adapting algorithms that compile and analyze user preferences about what they have been listening to for incorporation into the final remix.

This is an ambitious project and I will add more detail to the project as I elaborate deeper in part two.

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