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Stimulus reconstruction · Music · Neural decoding

Naturalistic music and neural representation

Master’s thesis linking naturalistic music stimuli to brain responses using computational modeling and a large-scale audio corpus.

Degree

MA in Digital Musics, Dartmouth

Themes

Naturalistic audio · encoding-style modeling

Role

Thesis lead

Overview

My Dartmouth thesis explored how complex, naturalistic music can be linked to human neural responses using computational modeling.

The project used a large audio corpus and neural data to ask how acoustic structure relates to perceptual and brain representations. It was one of my earliest substantial projects combining signal processing, modeling, and neuroscience, and it laid groundwork for later work in naturalistic stimuli, encoding models, and multimodal analysis.

Why it matters

This thesis was an early bridge between music technology, perception, and computational neuroscience. It also marks the point where my work began to converge on a consistent theme: using rich real-world stimuli, rather than simplified lab stimuli, to study brain function.

My role

Led the modeling work, built the computational framework, and developed the project as part of my MA in Digital Musics.