Here, you may read descriptions of courses and projects on the Signal Processing and Acoustics Master's programme at Aalborg University. Please also see the curriculum for the master's programme in Signal Processing and Acoustics. Here, you may find details on courses and projects as well as information on the programme’s legal basis, etc.
The programme is comprised by four semesters. The 1st semester is a common, and on the 2nd semester, you must choose a specialisation; Signal Processing and Computing or Acoustics and Audio Technology.
COMPULSORY FOR ALL NON-AAU BACHELORS
All bachelors enrolled in the programme who have not obtained their bachelor's degree from Aalborg University must take part in a course on problem based learning (PBL) as part of the 1st semester project. In case non-AAU bachelors get credit transfer for the 1st semester, they will be asked to take part in the course ensuring that they are trained in working according to the PBL-model. Read more about PBL here.
1st semester; applied signal processing
On the 1st semester, your project theme is “Applied Signal Processing”. In your project, you will learn about the field of signal processing, including how and when to apply it on real-world signals. You will gain knowledge on the nature of real-world signals impaired by noise, stochastic signals and how to model them mathematically, etc. In the project, you must demonstrate among others that you are able to identify and select between deterministic and stochastic methods according to the problem you are solving. Overall, your work should reveal your ability to successfully apply fundamental methods of the subject.
Fundamentals of Acoustics and Electro-acoustics
In this course, you will learn about the basic acoustic quantities, their physical significance and their role in the description of an acoustic process. In addition, you will study the principles of sound emission and reception. You will learn about acoustic transducers with regards to construction, mechanisms and use of different types of transducers, how they work electrically, mechanically and acoustically, enabling you to transform between the three. Upon the course, you must among others have acquired the skills to identify relevant acoustic variables for a given sound source and field and to model and measure acoustic transducers.
This course will introduce you to stochastic processes starting with definitions of a stochastic process, white-sense stationary (WSS) processes, Auto Regressive Moving Average (ARMA) processes, Markov models and Poisson point processes. The course will be accompanied by mini projects to properly combine the theoretical knowledge gained with practical hands-on experience. You will learn how to simulate stochastic processes through the courses and mini projects, and you will gain the appropriate engineering intuition of the basic concepts and results related to stochastic processes. This will enable you to design an appropriate model for a particular engineering problem involving randomness, derive solutions, assess the performance of these solutions and modify the model.
The purpose of this course is to give you an overview on the different classes of optimisation problems and associated methods for solving them. You will learn about objective function, global/local minima, constrained/unconstrained, convex/non-convex functions and sets. You will study linear and quadratic programming problems; Simplex method; Interior point methods with gradient, optimal gradient, Newton methods, line search and stop criteria; how to approximate a nonlinear convex problem with a linear one and how to solve combinatorial optimisation problems with methods such as Simulated Annealing (SA) and Genetic Algorithms (GA). Upon this course, you will understand how to formulate optimisation problems in signal processing and you will be able to design optimisation algorithms and evaluate their performance.
2nd semester; choose specialisation
On the 2nd semester, you must choose between two specialisations; Signal Processing and Computing (SPC) and Acoustics and Audio Technology (AAT). Both specialisations involve a specialised project and a course as well as one joint course.
SPC project theme (one is chosen):
- Scientific Computing
- Reconfigurable Computing
A project under the theme Scientific Computing will involve subjects such as computer architecture classification (Flynn’s taxonomy) and parallel computing techniques. Via the project work, you will learn about the relation between physical world problems and mathematical models. The objective is for you to become able to select suitable hardware platforms for different computational problems as well as to program solutions for such problems by use of various computational platforms.
If you choose to carry out your project under the theme of Reconfigurable Computing, you will focus on methodologies applied for resource optimal mapping of Digital Signal Processing (DSP) algorithms onto application-specific reconfigurable hardware/software platforms. You will work with analytical, numerical, experimental and simulation-based methods for assessing selected cost-function parameters typically associated with such systems.
- Reconfigurable and Low Energy Systems
For various types of applications, a software programmable digital signal processor is a suitable platform for real-time execution of a Digital Signal Processing (DSP) algorithm. However, in many cases, a much more flexible hardware platform where the designer can experiment with trade-offs between physical size of the circuitry, the overall execution time, the energy- and memory consumption and the numerical properties of an actual real-time implementation is highly needed. In this course, we therefore introduce theories and practical methods for the design and implementation of DSP algorithms onto reconfigurable platforms which provide the opportunity to optimise the combined algorithm/architecture solution in terms of specific design metrics. In particular, we will discuss how to represent and analyse DSP algorithms in terms of computational properties, and how to use this information in order to specify and design an algorithm-specific, resource-optimal, real-time hardware architecture. In this context, emphasis on the design for low energy consumption will be addressed in terms of hardware, but also in terms of embedded software. Upon the course, you will have a sound insight into the overall design trajectory for algorithm-specific, real-time DSP systems, and you will be able to apply a set of structured methods which are needed in order to improve the interaction between algorithms and architectures for selected design metrics.
AAT project theme (one is chosen):
- Sound Technology for the Normal Hearing
- Sound Technology for the Hearing-impaired
The AAT project themes treat the same subject under different circumstances; sound technology for the normal hearing and sound technology for the hearing-impaired. Both themes involve the subjects of audio engineering, hearing and human sound perception, and the objective of both themes is enabling you to select and apply analytical, numerical and experimental methods for analysis and design of complex audio systems. If you choose to work with sound technology for the normal hearing, your main focus will be on sound and audio technologies, including multi-channel sound recording and reproduction, measurement, instrumentation and standards. On the other hand, under the theme of sound technology for the hearing impaired, you will work with technical audiological methods, hearing aid technologies and problems in assessing and adapting hearing aids.
- Human Sound Perception and Audio Engineering
In this course, you will study the anatomy and physiology of the human ear, hearing diagnosis and disorders, as well as the fundamental properties of human sound perception, for example loudness, pitch, masking, spatial hearing and time/frequency resolution. Upon the course, you will have basic knowledge of among others multi-channel recording, storage and reproduction of sound, digital audio interfaces and standards and low noise audio design and interconnections.
Joint course for the two specialisations
- Scientific Computing and Sensor Modelling
The joint course for the two specialisations builds on the courses in Stochastic Processes and Optimisation Methods. You will learn about hardware and software platforms for scientific computing, programming techniques, profiling, benchmarking, code optimisation, etc., and you will become able to implement software programs to solve scientific computational problems using parallel computing as well as to debug, validate, optimise, benchmark and profile developed software modules.
3rd semester; specialisations continued
On the 3rd semester, the two specialisations are continued via individual courses. Each specialisation has its own course, and all students must attend one joint course and carry out the semester project under the same theme.
The project theme is Signal Processing and Acoustics. Overall, the project must demonstrate your ability to apply and implement appropriate methods and algorithms to a given problem, that you are able to select appropriate measurement and evaluation methods, and that you can analyse, design and implement engineering solutions to solve advanced problems. Projects involve advanced adaptive signal processing, sparse signal processing or machine learning algorithms for e.g., analysing and classifying signals from multiple sensors. Typical applications could be tracking objects, enhancing signals, extracting information from signals, classifying signals, synthesising signals (e.g., acoustic sound fields, images, etc.), navigations via radio communications (e.g., GPS).
- Machine Learning
The course in Machine learning gives you a comprehensive introduction to machine learning which is a field concerned with learning from examples and has roots in computer science, statistics and pattern recognition. The objective is realised by presenting methods and tools proven valuable and by addressing specific application problems.
- Architectural & Environmental Acoustics
Overall, this course focuses on room acoustics (for example sound fields in rooms, sound absorption and reflection, concert hall acoustics), building acoustics (for example sound transmission between rooms and into buildings, air borne sound transmission) and environmental acoustics (for example effect of noise on humans, noise from installations, noise barriers, noise assessment – ratings and noise descriptors, loudness calculation, assessment of annoyance and hearing impairment). You will learn to calculate and measure relevant room and building acoustical parameters, noise assessment parameters and noise barriers.
Joint course for the two specialisations
- Array and Sensor Signal Processing
This course in gives an overview of central signal processing algorithms and methods which can be applied on stationary or non-stationary signals such as speech, music, radar, sonar, electrocardiographic and radio signals. These signals often convey information about the physical process from which they originate, and analysing them is therefore useful in a wide range of applications. For example, analysing a signal generated by a waveform impinging on an antenna or microphone array enables tracking of a satellite, a person or the sun. Another application which is studied on the course is that of echo cancellation in which the acoustic echo of a speaking person is removed from a closed-loop system such as a telephone or a VoIP-system. In the course, a wide range of algorithms and methods are studied within fundamental statistical signal processing areas. These areas include spectral estimation, adaptive filtering, optimal estimation, array signal processing and multi-rate signal processing. Important prerequisites for following this course are therefore stochastic processing, convex optimisation and discrete-time signal processing.
On the 3rd semester, you also have the option of choosing an academic internship or a stay abroad at another university.
4th semester; master's thesis
On the 4th semester, you carry out your master's thesis within the specialisation you have studied on the previous semesters. This may be done either individually or in small groups. When you have finished the 4th semester, you are M.Sc. (Master of Science) in Signal Processing and Acoustics.