### Signal and Spectral Analysis: extracting information from noisy data (2021)

Welcome to Signal and Spectral Analysis: extracting information from noisy data (2021)

Description: In many situations, a number of observations are made which contain some information about an underlying phenomenon we are interested in. Examples of this are:

• The diagnosis of the Parkinson’s disease from a telephone recording,
• The assessment of bearing wear from vibrational data,
• The automatic transcription of music,
• Order tracking analysis of rotating machines,
• Automated analysis of the heart sound, and
• Harmonic analysis in power systems.

To solve these and many other problems, a signal analysis toolbox is needed. This course focuses ondeveloping, explaining, understanding, and using such tools. Specifically, the course covers important and general concepts such as:

• Signal modelling: Which models exist and what are their applicability and limitations?
• Spectral analysis: Why is signal analysis often performed as a function of frequency and how do you do it?
• Inference and parameter estimation: How do you estimate model parameters accurately and quantify how well you do?

The course is primarily developed for doctoral students from medicine and various engineering and natural science disciplines who wish to not only apply, but also to understand signal and spectral analysis. Consequently, the course is rooted in a principled and systematic exposition of fundamental concepts and tools and in a scientific approach which promotes the creation of knowledge over improving state-of-the-art by ε percent. An important goal of the course is to make doctoral students able to solve a signal and spectral analysis task based on data from their own Ph.D.-project. This is integrated in the course via a mini project.

Keywords: Filtering, statistical signal processing, estimation theory, maximum likelihood, powerspectral density estimation, modelling, least squares, autoregressive, nonnegative matrix factorizations, sparsity, periodic signals, Fourier analysis, line spectra.

Prerequisites: Basic probability theory, linear algebra, signal processing, and experience with MATLAB and/or Python programming.

Organizer: Professor Mads Græsbøl Christensen - mgc@create.aau.dk

Lecturers:

ECTS: 3.0

Time: 25-29 October 2021

Place: Aalborg University - This course will be conducted with ONLINE attendance

Zip code:
9220

City: Aalborg

Number of seats: 30