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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
- 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.
- Graduation Semester
- 2025-08
- Type of Resource
- Thesis
- Handle URL
- https://hdl.handle.net/2142/129827
- Copyright and License Information
- Copyright 2025 Krishna Subramani
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