I discovered Ableton when I was in 9th grade in middle school. I remember stumbling upon this software while watching a YouTube video where a producer was recreating Daft Punk's track One More Time from the original sample. Even though the interface wasn't very appealing at first glance, I was fascinated by the producer's manipulations. As the video progressed, the original track was cut, pasted, sped up, and gradually the Daft Punk title became recognizable.
I then learned how to use the software. I was impressed by all the possibilities this tool offered. The more I used it, the more my perception of music changed. From then on, whenever I heard a piece of music, my brain naturally tried to visualize the different tracks in the music (percussions, synth, bass, etc.).
My exploration led me to discover the vibrant world of SoundCloud, where artists engaged in a creative dialogue by sampling and reimagining each other’s work.
As I continued to make music, I began to feel frustrated. I often wanted to take a piece of music and make a different version of it. In general, it's best to have access to the acapella, or the singing part, of a track. Although some websites listed available acapellas, it was very rare to find the right one. Often, I would come across scams or poorly filtered acapellas where you could still hear some of the instrumental music in the background. Back then, if we didn't have access to the original voice file recorded in the studio, there were several ways to try to separate the voice from the music. We could use filters, or if we had the instrumental version, we could try the phase cancellation technique where we "subtract" this track from the whole track, leaving only the voice. But these methods rarely gave good results.
Nine years later, in 2019, I discovered something amazing. In the meantime, I had learned about computers, learned how to code, and finished my studies in machine learning. That year, the research teams at Deezer released Spleeter, a tool designed to separate the different parts of any piece of music. At that moment, I was overwhelmed: all music had become material that we could take apart.
This project, The Blurry Mix is a patchwork of sounds from popular classics, captured using a source separation tool. In this mix, I dilute hit songs from the past few decades in a blend of more experimental pieces and personal compositions.
This mix merges different influences, placing a higher emphasis on distinct sounds rather than rhythm. My goal was to create a minimalist output to highlight the different samples, which is why I deliberately reduced the use of drums.
Four songs in this mix (respectively from Kanye West, Nelly Furtado, Justin Timberlake and Beyonce), have been completely reimagined. They are noted as « blurry versions ».
Have a good listening!