Implicit neural representations for time-frequency signal processing
Subramani, Krishna
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https://hdl.handle.net/2142/129827
Description
Title
Implicit neural representations for time-frequency signal processing
Author(s)
Subramani, Krishna
Issue Date
2025-06-24
Director of Research (if dissertation) or Advisor (if thesis)
Smaragdis, Paris
Doctoral Committee Chair(s)
Smaragdis, Paris
Committee Member(s)
Kim, Minje
Hasegawa-Johnson, Mark
Choudhury, Romit Roy
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Time-frequency Representations
Point Clouds
Implicit Neural Representations
Multichannel Filtering
Machine Learning
Signal Processing
Language
eng
Abstract
This dissertation presents a departure from conventional audio signal processing approaches that rely on fixed-dimensional vector representations and regularly sampled time-frequency grids. We introduce a flexible framework that models audio time-frequency representations using continuous, adaptive structures, enabling greater robustness and efficiency in modern applications. First, we propose a differentiable proxy for automatically optimizing Short-Time Fourier Transform parameters, aligning time-frequency resolution with task-specific objectives. We then reformulate time-frequency representations as Point Clouds, allowing for resolution-invariant processing and effective subsampling without sacrificing performance. Building on this foundation, we employ Implicit Neural Representations to model vectors and filters as continuous functions, thereby decoupling classical algorithms like multichannel filtering and matrix factorization from fixed parameters and sampling constraints. These contributions collectively propose a unified, parameter-agnostic view of signal processing that seamlessly integrates traditional methods with modern learning-based paradigms.
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