Teaching Machines to Hear: How MIT’s Paris Smaragdis is Revolutionizing Artificial Perception
The sharp crack of a baseball meeting a bat, instantly followed by the thunderous roar of a stadium crowd, can instantly capture a fan’s attention even if they momentarily missed seeing the home run itself. While human perception heavily relies on sight to navigate the physical world, our auditory faculties provide an equally vital, continuous stream of environmental data. To bridge the gap in artificial intelligence, Paris Smaragdis, an alumnus and newly appointed professor at the Massachusetts Institute of Technology (MIT), is pioneering a new frontier in machine perception: designing computer systems that can hear, interpret, and understand sound with an acuity that matches, and in some cases surpasses, human capabilities.
This burgeoning field of machine listening holds profound implications for numerous industries. A machine endowed with super-human acoustic perception could instantly pinpoint the precise location of a catastrophic traffic collision by isolating the distinct frequencies of squealing tires and crunching metal. In heavy industry, an advanced acoustic sensor could preemptively identify the subtle acoustic signatures of a drill bit fracturing while boring through dense bedrock during the creation of a geothermal energy well, thereby averting costly equipment failures and safety hazards.
"I like to say that I do the same thing the computer vision folks do, but instead I do it for hearing," explains Smaragdis. Holding a dual appointment as a professor in the Music and Theatre Arts program within the School of Humanities, Arts, and Social Sciences, as well as a shared position in the Department of Electrical Engineering and Computer Science and the MIT Schwarzman College of Computing, Smaragdis is uniquely positioned at the intersection of technology and the arts. His research group investigates advanced machine learning applications for sound source separation, smart devices designed to augment human auditory environments, and computational tools that empower individuals to analyze, manipulate, and create music.
A Musical Prelude: From Synthesizers to Computer Science
The foundations of Smaragdis’s unorthodox academic and professional trajectory were laid during his childhood in Greece. Fascinated by classical guitar at a young age, his musical perspective shifted fundamentally when his father introduced a small, programmable synthesizer into the household.
"That little synthesizer had a function that allowed you to make your own sounds, and I became completely captivated by the possibilities," Smaragdis recalls. This discovery coincided with the 1980s pop music landscape, where synthesizers were rapidly taking center stage, cementing his lifelong fascination with electronic sound generation.
As he approached higher education, Smaragdis sought an academic program dedicated to computer-generated music. Finding few options within traditional European conservatories of the era, he chose to enroll at the Berklee College of Music in Boston. There, he immersed himself in a curriculum that utilized computers and synthesizers to reshape and manipulate sound. Seeking further creative challenges, he declared the bass guitar as his major, drawn to jazz fusion ensembles.
"I found the bass to be sonically more interesting than the guitar. It was a more unexplored domain, and I’ve always gravitated toward unexplored domains," he notes. This intellectual curiosity propelled him to collaborate with mentor Richard Boulanger on early software tools for computer-assisted music production.
His undergraduate work eventually bridged into a transformative collaboration with Professor Barry Vercoe at the MIT Media Lab, a pioneer in computer music whose legacy resonates deeply within the institution. Following his work with Vercoe, Smaragdis pursued graduate studies at MIT, completing both his master’s degree and his PhD, followed by a postdoctoral appointment.
"In the Media Lab, I got exposed more to the computer part of computer music. That’s where my attention shifted a bit. Now I really wanted to make computers that could understand sound," he says.
Tuning in to Unexplored Domains: Chronology of an Academic and Industry Pioneer
While contemporary researchers in the Media Lab predominantly relied on traditional signal processing techniques to manage audio data, Smaragdis charted an independent course by adopting machine learning methodologies. This early bet on artificial intelligence yielded profound advantages over conventional signal processing frameworks.
"Over time, it became really clear to me that we need to teach computers how to listen. We can’t just tell them how to listen. If anything, we don’t even know how we humans listen," Smaragdis reflects. "We don’t have a vocabulary to explain what it means to listen, or how to tell the difference between a cat’s meow and the sound of a violin."
Following his postdoctoral tenure at MIT, Smaragdis transitioned into private-sector research, spending time at Mitsubishi Electric Research Labs. During this period, he developed pioneering AI systems capable of scanning video soundtracks to interpret on-screen events automatically. By focusing on auditory cues—such as a sudden surge in crowd noise during a soccer match—his algorithms successfully identified game highlights without requiring complex computer vision analysis. This technology was subsequently integrated into consumer personal video recorders, traffic monitoring systems, and mobile telecommunications devices.
He later joined Adobe, founding an audio research team dedicated to creating accessible tools that enabled artists and producers to manipulate sound without requiring specialized technical expertise. Driven by a desire to solve increasingly complex foundational engineering problems, Smaragdis transitioned back to academia, accepting a faculty position at the University of Illinois at Urbana-Champaign (UIUC).
During his 15-year tenure at UIUC, Smaragdis and his research group developed advanced neural networks capable of cleanly separating individual audio streams from complex, overlapping sound mixtures. This breakthrough technology caught the attention of filmmakers working on the acclaimed 2021 documentary miniseries The Beatles: Get Back. Enlisted to clean up degraded archival audio recordings of the iconic band, Smaragdis applied his machine learning models to untangle difficult acoustic environments—such as simultaneous overlapping dialogue, playful banter, and musical improvisation by John Lennon, Paul McCartney, and George Harrison. This innovative approach to sound restoration contributed to the production team winning a Primetime Emmy Award for Outstanding Sound Editing for a Nonfiction or Reality Program in 2022.
A Concerted Effort at MIT
Returning to MIT in 2025 as a member of the faculty and the Research Laboratory of Electronics, Smaragdis aims to tackle the multifaceted interdisciplinary challenges currently facing the field of machine perception. His modern research lab operates along two primary pillars: developing advanced tools to assist musicians without displacing human creativity, and engineering fundamental algorithms for universal audio processing.
Among his team’s latest projects is the exploration of signal-processing algorithms that operate independently of fixed frames of reference, such as rigid sampling rates, frame sizes, or context-windows. By dismantling these traditional computational constraints, Smaragdis believes his team can unlock vastly more efficient and flexible audio processing architectures. Furthermore, his group is actively integrating large language models and advanced generative AI frameworks into modern speech recognition and audio enhancement pipelines.
"AI is an immensely powerful tool, but I don’t think we have found the ideal way to use it for sound and music yet," he observes.
Beyond his research responsibilities, Smaragdis maintains a deep commitment to student mentorship, employing hands-on pedagogical approaches to help students translate abstract mathematical equations into tangible, working software. Outside of the laboratory, he remains active in Boston’s vibrant jazz scene as a bassist and participates competitively in underwater hockey—a demanding sport requiring participants to maneuver a weighted puck along the bottom of a swimming pool while wearing snorkeling gear. For Smaragdis, both his unconventional athletic pursuits and his pioneering computer science research stem from the same underlying motivation: a relentless drive to explore uncharted territory.
"I’ve always liked doing things that were different from what everyone else was doing," Smaragdis concludes. "If you can create a new sound that no one has heard before, what does that mean about your ability to create new things? I still find these questions completely fascinating."
