Signal and Spectral Analysis: extracting information from noisy data

Welcome to Signal and Spectral Analysis: extracting information from noisy data (2026)

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: Assoc. Professor Jesper Rindom Jensen - jrj@es.aau.dk

Lecturers: Assoc. Professor Jesper Rindom Jensen - jrj@es.aau.dk

ECTS: 3.0

Time: 18 - 22 May, 2026 

Place: Aalborg University

City: Aalborg

Number of seats: 30

Deadline: 28 April, 2026

Important information concerning PhD courses: 

There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course..

For external PhD students: This course is a general course and is prioritised for PhD Students enrolled at Aalborg University. If there are available seats, PhD students from other universities will be accepted. You will be notified shortly after the deadline if you have been accepted.

To attend courses at the Doctoral School in Medicine, Biomedical Science and Technology you must be enrolled as a PhD student.

We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at phdcourses@adm.aau.dk  When contacting us please state the course title and course period. Thank you.