Author: Office of the Vice Chancellor<\/p>\n
Rhythmic flashes from a computer screen illuminate a dark room as sounds fill the air. The snare drum sample comes out crisp and clean by itself, but turns muddy in the mix, no matter how the levels are set. Welcome to the world of modern music-making \u2014 and its discontents.<\/p>\n
Today\u2019s 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.<\/p>\n
\u201cThere\u2019s a lot of manual searching to get the right musical result, which can be distracting and time-consuming,\u201d 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.<\/p>\n
Called Samply, Swaney\u2019s 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.<\/p>\n
\u201cSamply organizes samples based on their sonic characteristics,\u201d explains Swaney. \u201cThe result is an interactive plot where similar sounds are closer together and different sounds are farther apart. Samply allows multiple sample libraries to be visualized simultaneously, shortening the lag between imagining a sound in your head and finding it.\u201d<\/p>\n
For Swaney, the development of Samply drew on both his research expertise and personal life. Before coming to MIT, he had produced albums with indie musicians including Eric Schirtzinger, a drummer and co-creator of the tool. The two recorded drums in a basement and tried to improvise with cheap hardware and hacks \u2014 like hanging rugs from the ceiling to dampen reverberation. \u201cThe constraints made us get creative,\u201d says Schirtzinger, who is now a computer science major at the University of Wisconsin at Madison.<\/p>\n
That creativity was further honed after Swaney completed 6.862 (Applied Machine Learning). He saw an opportunity to rekindle his music production hobby by applying what he had learned from the project-based course, devising a way to automate the search for the right samples when producing a new song.<\/p>\n
\u201cI figured the computer could listen to samples much faster than I could,\u201d he says. Beyond the clever use of machine learning, the real magic of Samply is that conceptually, it is founded on a deep understanding of what it takes to make music. \u201cWe aren\u2019t just AI enthusiasts applying machine learning to music,\u201d says Schirtzinger. \u201cWe are musicians who want better tools for making music.\u201d<\/p>\n
It turns out that at MIT, they aren\u2019t the only ones with a song in their hearts. While presenting Samply at the Schwarzman College of Computing exposition last winter, dozens of faculty, staff, and students gathered around Swaney\u2019s poster and live demonstration to exchange ideas. Some had years of experience producing music with professional software, while others simply appreciated the visualizations and sounds in the demo.<\/p>\n
Spurred by the interest in Samply at the exposition, Swaney and Shirtzinger are in the process of turning their project into a startup company. As a first step, the two reached out to the Technology Licensing Office (TLO) for advice, which referred them to the Venture Mentoring Service (VMS).<\/p>\n
Samply joined VMS in April and was paired with two MIT-affiliated mentors and entrepreneurs, Stephen Bayle and John Stempeck. After pitching Samply to his mentors, Swaney received sage advice on a crafting a business plan and sales strategy, and then began making connections with others interested in music technology as a business.<\/p>\n
Samply has since been accepted into the ELEVATE accelerator, sponsored by the local digital marketing firm HubSpot, and Swaney is applying for seed funding through the MIT Sandbox Innovation Fund.<\/p>\n
\u201cStarting a company as a student can be daunting, but the MIT community gives us confidence,\u201d he says. \u201cIf we can\u2019t do it at MIT, then where can we?\u201d<\/p>\n
In fact, the time and attention he has spent on Samply has had an \u201calmost paradoxical\u201d benefit to his academic life as a graduate student. \u201cI was spending all of my time in the lab,\u201d he says. \u201cWhen I took a step back to make Samply, I could see the forest from the trees in my research.\u201d<\/p>\n
Swaney found that focusing on his love of music served as an \u201cemotional outlet,\u201d 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 \u201cmy work on single-cell analysis.\u201d Applying the method to the tool clarified his thinking in the biological realm, leading to a new method to produce better\u00a0clustering, or a way to better sort, recognize, and visualize groups of cells. \u201cIt was a bit like a\u00a0musical\u00a0theme and variation, but\u00a0with\u00a0my research,\u201d Swaney says.<\/p>\n
As for Samply, there will be a free beta version of the app launching in September, and a Kickstarter campaign is due in the coming year to fuel future developments.<\/p>\n
\u201cWe want to\u00a0get Samply\u00a0into the hands of more producers\u00a0and content creators\u00a0so that we can\u00a0establish a\u00a0feedback loop\u00a0that guides\u00a0our priorities,\u201d he says. \u201cOur technology may\u00a0also\u00a0have\u00a0applications in live\u00a0music performance, instrumentation, and in film and videography. We are excited to\u00a0explore those possibilities.\u201d<\/p>\n<\/div>\n
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