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| Chemical engineering graduate student Justin Swaney is applying machine learning to music production. “There’s a lot of manual searching to get the right musical result, which can be distracting and time-consuming,” says the co-creator of a new tool to help producers find just the perfect sound. Photo: Lillie Paquette |
Today’s digital music producers face a common dilemma: how to mesh samples that may sound great on their own but do not necessarily fit into a song like they originally imagined. One solution is to find and audit dozens of different samples, a tedious process that can take time to finesse.
“There’s a lot of manual searching to get the right musical result, which can be distracting and time-consuming,” says Justin Swaney, a PhD student in the MIT Department of Chemical Engineering, a music producer, and co-creator of a new tool that uses machine learning to help producers find just the perfect sound.
Called Samply, Swaney’s visual sample-library explorer combines music and machine learning into a new technology for producers. The top winner at the MIT Stephen A. Schwarzman College of Computing Machine Learning Across Disciplines Challenge at the Hello World celebration last winter, the tool uses a convolutional neural network to analyze audio waveforms...
Swaney found that focusing on his love of music served as an “emotional outlet,” helping to mitigate intellectual burnout. Although Samply may have taken him away from the lab bench, it has also ended up informing his research. The original idea of visualizing samples, he says, stemmed from “my work on single-cell analysis.” Applying the method to the tool clarified his thinking in the biological realm, leading to a new method to produce better clustering, or a way to better sort, recognize, and visualize groups of cells. “It was a bit like a musical theme and variation, but with my research,” Swaney says.
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Source: MIT News
