Introduction

My research explores how machines can make sense of music and the sounds around us. Together with my students, I develop signal processing and machine learning methods that help computers listen, interpret, and reason about sound.

I’m particularly interested in how sound connects with vision and language, what machine learning models actually understand about the acoustic world, and how we can make them more robust, interpretable, and reliable. Our work spans music information retrieval, environmental sound understanding, multimodal analysis and synthesis, and applications in accessibility and health.

Selected work

Recent student-led projects.

Three cards describing speech, music, and sound effects in a mixed scene

Preprint · Sound scenes

MixCards

Structured descriptions of sound scenes made of several overlapping sources.

Diagram of acoustic evidence used to support a reasoning trace

Preprint · Trustworthy audio

TRACE

Reasoning about sound from acoustic cues and evidence that can be checked.

PDF
A point-cloud scan split by height, beside reference and predicted acoustic maps

Preprint 2026 · Room acoustics

Predicting Acoustic Fields from Point-Cloud Scans

Estimating how a room sounds from a point-cloud scan of the space.

Binaural separation ratings beside an interaural-time-difference scatter plot

ISMIR 2026 · Source separation

Spacing Out

How reliable the usual metrics are for binaural music source separation.

Localization maps in two rows and five columns: dinosaur, firetruck, silence, noise, and off-screen sound for SSL-Align and SSL-SaN

BMVC 2025 · Audio-visual learning

Learning from Silence and Noise

Visual sound source localization that learns from silence, noise, and off-screen sounds, not only from positive audio-visual pairs.

A music representation model beside accuracy plotted against reconstruction error

WASPAA 2025 · Music representations

Balancing Information Preservation and Disentanglement

Self-supervised music representations that keep musical detail while separating the factors that shape it.

Awards

NYU Steinhardt Gabriel Carras Research Award for Promising Young Scholars.

Google Award for Inclusion Research

NYU Provost’s Postdoctoral Fellowship.

Best undergraduate thesis award, National Academy of Engineering of Uruguay.

Research support

Our work has been supported by the National Science Foundation, the National Institutes of Health, Google, Adobe, and NYU, in collaboration with colleagues at NYU and NJIT.

HCC Medium

AI-supported audio captioning of non-speech information, with Mark Cartwright and Sooyeon Lee (NJIT).

NIH R34

Feasibility of a music listening intervention for mental health recovery after stroke, with Pablo Ripollés and Heidi Schambra.

Award for Inclusion Research

Including Latin American culture in music information research.

Research gift

Support for research in audio understanding.

MARL seed grants

AI-assisted tools for music therapy, and earlier work on sound captioning for accessibility.