### Signal and Spectral Analysis: extracting information from noisy data

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

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 on developing, explaining, understanding, and using such tools. Specifically, the course covers important and general concepts such as:

Signal modelling: Which models exist, what are their applicability and limitations, and how do you compare different models?

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 and how certain you are?

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, Bayesian statistics, separation, modelling, least squares, enhancement, nonnegative matrix factorizations, periodic signals, Fourier analysis.

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

Organizers Prof. Mads Græsbøll Christensen and Ass. Prof. Jesper Kjær Nielsen

Lecturers: Prof. Mads Græsbøll Christensen and Ass. Prof. Jesper Kjær Nielsen
ECTS: 3

Time: September 30th-October 4th, 2019, all days.

Place:

City:

Number of seats: 30