**Contact Info**

Department of Applied Mathematics

University of Waterloo

Waterloo, Ontario

Canada N2L 3G1

Phone: 519-888-4567, ext. 32700

Fax: 519-746-4319

PDF files require Adobe Acrobat Reader

Wednesday, May 4, 2022 10:00 AM EDT

MS Teams (please email amgrad@uwaterloo.ca for the meeting link)

Nicholas Richardson | Applied Mathematics, University of Waterloo

A Sparse Random Feature Model for Signal Decomposition

Signal decomposition and multiscale signal analysis provide useful tools for time-frequency analysis. In this thesis, an overview of the signal decomposition problem is given and popular methods are discussed. A novel signal decomposition algorithm is presented: Sparse Random Mode Decomposition (SRMD). This method sparsely represents a signal as a sum of random windowed-sinusoidal features before clustering the time-frequency localized features into the constituent modes. SRMD outperforms state-of-the-art methods on a variety of mathematical signals, and is applied to real-world astronomical and musical examples. Finally, we discuss a neural network approach to tackle challenging musical signals.

Event tags

**Contact Info**

Department of Applied Mathematics

University of Waterloo

Waterloo, Ontario

Canada N2L 3G1

Phone: 519-888-4567, ext. 32700

Fax: 519-746-4319

PDF files require Adobe Acrobat Reader

University of Waterloo

University of Waterloo

43.471468

-80.544205

200 University Avenue West

Waterloo,
ON,
Canada
N2L 3G1

The University of Waterloo acknowledges that much of our work takes place on the traditional territory of the Neutral, Anishinaabeg and Haudenosaunee peoples. Our main campus is situated on the Haldimand Tract, the land granted to the Six Nations that includes six miles on each side of the Grand River. Our active work toward reconciliation takes place across our campuses through research, learning, teaching, and community building, and is co-ordinated within the Office of Indigenous Relations.