Mason L. Wang

I am an EECS PhD student at MIT CSAIL. My research is at the intersection of audio, machine learning, and signal processing.

I received my master's in Electrical Engineering at Stanford University working at the Stanford Vision and Learning Lab. I was advised by Jiajun Wu and Mert Pilanci.

You can contact me at ycda [at] stanford [dot] edu. Or, you can find me on Twitter, LinkedIn.

Research Interests

Capturing Real Auditory Scenes: Recently, my work has been on virtualizing real auditory scenes and acoustic spaces. For instance, imagine being able to capture a video of a concert, and then moving around the concert space freely. Imagine taking several videos of a fireworks show, and compiling them into an interactive 3D experience. Perhaps you could capture the intrinsic acoustic properties of your living room, in a way that allows you to listen to your favorite artist there.

Differentiable and Inverse Audio Rendering: Audio renderers often require slow and non-differentiable techniques. This makes it difficult to fit to real scenes via gradient-based optimization processes, and thus, often results in audio simulations that are not accurate to the real-world sounds they attempt to replicate. Inspired by visual inverse rendering and capture techniques, I believe combining physical inductive biases with machine learning can help us fit simulations to real scenes, and thus make them more accurate.

AI assisted Sound Design and Music-making: Making music requires many steps: writing melodies/themes, chord progressions, arrangement, sound design, mixing, mastering, etc. Some musicians are more inclined towards certain parts of this process. My goal is to provide musical artists with controllable assistance for parts of the music-making process they are unfamiliar with.

Publications
	Hearing Anything Anywhere Mason L. Wang, Ryosuke Sawata, Samuel Clarke, Ruohan Gao, Elliott Wu, Jiajun Wu CVPR, 2024 video / paper / website / dataset / code / arkiv We create a method of capturing real acoustic spaces from 12 RIR measurements, letting us play any audio signal in the room and listen from any location/orientation. We develop an 'audio inverse-rendering framework' that allows us to synthesize the room's acoustics (monoaural and binaural RIRs) at novel locations and create immersive auditory experiences (simulating music).
	SoundCam: A Dataset for Finding Humans Using Room Acoustics Mason L. Wang, Samuel Clarke, Jui-Hsien Wang, Ruohan Gao, Jiajun Wu NeurIPS Datasets and Bencmharks, 2023 project page / video / arXiv Humans induce subtle changes to the room's acoustic properties. We can observe these changes (explicitly via RIR measurement, or by playing and recording music in the room) and determine a person's location, presence, and identity.
	RealImpact: A Dataset of Impact Sound Fields for Real Objects Samuel Clarke, Ruohan Gao, Mason L. Wang, Mark Rau, Julia Xu, Jui-Hsien Wang, Doug James, Jiajun Wu CVPR, 2023 (Highlight, Top 2.5% of Submissions) project page / video / arXiv Everyday objects possess distinct sonic characteristics determined by their shape and material. RealImpact is the largest dataset of object impact sounds to date, with 150,000 recordings of impact sounds from 50 objects of varying shape and material.

Education and Experience
	MIT August 2024-? EECS PhD Student Cambridge, Massachusetts
	SONY AI June 2024-August 2024 Research Intern, Music Foundation Model Team Tokyo, Japan
	Stanford University September 2022-June 2024 M.S. in Electrical Engineering, specialization in Signal Processing and Optimization GPA: 4.22/4.3 Course Assistant for ENGR 108 (3x), EE 178 (1x) Research Assistant in CS (1x), EE (1x)
	The University of Chicago October 2018-June 2022 B.S. in Computer Science with a Specialization in Machine Learning B.A. in Mathematics GPA: 4.0/4.0 Honors: Odyssey Scholar, Enrico Fermi Scholar, Robert Maynard Hutchins Scholar, Summa Cum Laude