- Advanced Transform Methods
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Advanced Transform Methods
Time-frequency transforms are an important tool in the analysis and processing of signals and images. These transforms include the Fourier transform, spectrogram, discrete cosine transform, wavelet transform, and Wigner-Ville distribution. This course will introduce these various transforms and explore how they are suitable for different signal and image processing applications.
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Advanced Transform Methods
Summary:
Time-frequency transforms are an important tool in the analysis and processing of signals and images. These transforms include the Fourier transform, spectrogram, discrete cosine transform, wavelet transform, and Wigner-Ville distribution. This course will introduce these various transforms and explore how they are suitable for different signal and image processing applications.
Credits:
15.0
Pre-requisites:
ELE502 or ELEM020
Aims:
This course aims to introduce transform and sub-band techniques as a pre-cursor to compression and other applications. It is the first step beyond the fundamentals of Digital Signal processing.
Objectives:
Recall a range of joint time-frequency transforms.
Discuss the relative merits of different transforms.
Employ the common mathematical framework underlying many transform methods.
Derive various properties of different transforms.
Use high performance mathematical visualization software (e.g. Matlab) to implement these transforms.
Apply these transforms to signal and image processing problems, such as compression or denoising.
Core Skills:
Analyse information using mathematical models
Books:
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way, by S.G. Mallat; Academic Press, ISBN: 13: 978-0-12-374370-1
- Fundamentals of DSP
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Fundamentals of DSP
The purpose of this module is to introduce the general topic of Digital Signal Processing and bring students up to a common level. Students are first introduced to the behaviour of simple filters as LTI systems, represented by difference equations. Frequency response of these systems leads into the study of Discrete Fourier Transform and simple Spectral Analysis. There follow sections on designing the coefficients of LTI systems so they can be programmed to perform as filters to prescribed magnitude specifications.
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Fundamentals of DSP
Summary:
The purpose of this module is to introduce the general topic of Digital Signal Processing and bring students up to a common level. Students are first introduced to the behaviour of simple filters as LTI systems, represented by difference equations. Frequency response of these systems leads into the study of Discrete Fourier Transform and simple Spectral Analysis. There follow sections on designing the coefficients of LTI systems so they can be programmed to perform as filters to prescribed magnitude specifications.
Credits:
15.0
Aims:
This module will introduce the general topic of Digital Signal Processing and bring students from diverse backgrounds up to a common level for Msc studies in DSP. This is done through lectures and an intensive laboratory programme.
Objectives:
Describe the basic operations of DSP of addition, multiplication and memory/delay.
Employ these simple operators in combination to perform useful tasks such as filtering and spectral analysis.
Analyse simple filter structures both analytically and empirically (using Matlab).
Design digital filters to meet specifications.
Formulate the parameters of a spectral analysis system to obtain adequate resolution for the given task.
Core Skills:
This course is designed to bring all MSc DSP students up to a common level.
All subsequent Signal processing courses in the programme are either directly or indirectly dependent on ELEM020.
Books:
Digital Signal Processing: a computer based approach by Sanjit Mitra; McGraw-Hill 2005; ISBN 0071244670
- Digital Audio Effects
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Digital Audio Effects
This course introduces digital audio effects and related subjects. The main emphasis will be on the use of digital signal processing and its applications to the creation or modification of sounds and sound effects.
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Digital Audio Effects
Summary:
This course introduces digital audio effects and related subjects. The main emphasis will be on the use of digital signal processing and its applications to the creation or modification of sounds and sound effects.
Credits:
15.0
Aims:
This course covers the entire field of digital audio effects, including some depth in the subfields and related subjects. It is concerned with the use of digital signal processing and its applications to the creation or modification of sounds and sound effects. It explains what can be done in the digital processing of sounds in the form of computer algorithms and sound examples resulting from these transformations. It describes signal processing concepts and software implementations, as well as advances in filters, delays, modulators, and time-frequency processing of sound. It primarily cover time-domain, non- linear, time-segment, time-frequency, source-filter, spectral, bitstream signal processing, spatial effects, time and frequency warping, and the control of audio effects.
It is a core component necessary to provide engineering students with training in advanced music and audio technologies, and to give them the technical background and skills they need to create the tools used in audio production, audio engineering, and broadcasting. The lectures assume that the students have some basic knowledge of digital signal processing, and build upon that knowledge to teach students how to analyze and modify any musical audio signal. The lectures will use standard teaching materials and sample MATLAB code and audio files. The students will also gain an understanding of the creation, modification and modelling of digital audio, and understand how the complex algorithms are used in many common applications.
Objectives:
Methods to create a physical model of a sound or musical instrument.
How audio signals are filtered, and which filter is appropriate for a given task.
How to create common effects, such as wah-wah, flange, vibrato, tremolo, trill, echo...
How time segment processing is performed.
Specialised processing techniques often applied to audio, such as nonlinear and spectral processing, and when and how they are applied.
Phase vocoder technology and how phase vocoders are used to create effects.
How to do time scaling and pitch shifting on a signal, and how different methods are applied for speech and music, and for polyphonic and monophonic situations..
The effects of 3d sound, and how to reproduce a 3 dimensional effect using a minimum number of channels.
Gain a general knowledge of how audio effects are produced, and how each of the audio effects mentioned in class fit into a general theory of audio analysis and effect creation.
Books:
DAFX - Digital Audio Effects by Udo Zoelzer (Editor); John Wiley & Sons 2002; ISBN 0471490784
- Music and Speech Processing
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Music and Speech Processing
This course aims to introduce students to the application of Digital Signal Processing to music and speech.
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Music and Speech Processing
Summary:
This course aims to introduce students to the application of Digital Signal Processing to music and speech.
Credits:
15.0
Pre-requisites:
ELE502, ELEM020, ELEM018
Aims:
The application-oriented nature of the syllabus will reinforce the theory learned in other courses through lectures and laboratories.
Objectives:
Describe the physiology and physics involved in sound production and perception.
Demonstrate how various fundamental concepts in Digital Signal Processing can be combined into systems, like a Digital Power Amplifier.
Demonstrate how higher level processing components are constructed from lower level ones.
Discuss how compression of speech and of music, though similar, have different requirements.
Propose specific compressors for specific applications.
Identify latest innovations in this area involving delivery formats such as Internet and DVD.
Position their acquired knowledge in a commercial context.
Core Skills:
Students will be able to evaluate and design complex signal processing systems.
Books:
Speech and Audio Signal Processing: Processing and Perception of Speech and Music by B.Gold and N.Morgan; John Wiley and Sons 1999; ISBN 0471351547
- Introduction to Computer Vision
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Introduction to Computer Vision
New module under development for 2012/13. Information pertaining to this module will appear once approved.
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Introduction to Computer Vision
Summary:
New module under development for 2012/13. Information pertaining to this module will appear once approved.
Credits:
15.0
Aims:
New module under development for 2012/13. Information pertaining to this module will appear once approved.
Objectives:
New module under development for 2012/13. Information pertaining to this module will appear once approved.
Core Skills:
New module under development for 2012/13. Information pertaining to this module will appear once approved.
- Music Analysis and Synthesis
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Music Analysis and Synthesis
This course introduces students to common methods for the analysis and synthesis of digital audio. It presents in-depth studies of general approaches to the low-level analysis of audio signals, and follows these with specialised methods for the semantic analysis of music signals, including the extraction of information related to the rhythm, melody, harmony and instrumentation of recorded music. This is followed by an examination of the most important methods of sound synthesis, including wavetable, sampling, additive, subtractive, modulation, and physical modelling synthesis
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Music Analysis and Synthesis
Summary:
This course introduces students to common methods for the analysis and synthesis of digital audio. It presents in-depth studies of general approaches to the low-level analysis of audio signals, and follows these with specialised methods for the semantic analysis of music signals, including the extraction of information related to the rhythm, melody, harmony and instrumentation of recorded music. This is followed by an examination of the most important methods of sound synthesis, including wavetable, sampling, additive, subtractive, modulation, and physical modelling synthesis
Credits:
15.0
Pre-requisites:
ELEM020 and ELEM018
Aims:
This module is intended to provide students with advanced training in standard and state-of-the-art techniques for music analysis and synthesis. This knowledge is relevant for the music generation, processing, recording, reproduction and distribution industries, with special emphasis on music-oriented on-line services and the development of software and hardware for musicians, musicologists, sound engineers and producers. Background in digital signal processing is essential for the understanding of analysis and synthesis processes on musical signals.
Objectives:
- Understand the specific issues involving the analysis and synthesis of musical signals and the use of the presented techniques in real-world applications.
- Gain a deep understanding of sound synthesis using linear and non-linear methods
- Understand and gain practice in the use of sinusoidal models and the phase vocoder as analysis/synthesis tools.
- Learn about high-level music analysis techniques for event detection, beat tracking and pitch estimation in monophonic and polyphonic music.
- Students will also be introduced to advanced topics related to the state-of-the-art in music analysis and its applications.
Books:
Musical Signal Processing by C. Roads, S. Pope, A Piccialli and G De Poli (Editors); Swets and Zeitlinger 1997; ISBN 9026514824
- The Semantic Web
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The Semantic Web
The idea of putting semantic information on the Web has been around for a long time: we now have the beginnings of a practical application. This has its foundations in what is
called Description Logic, which strikes a good balance between tractability and usability. This has led to a Web
language called OWL, which is at the centre of modern work on the Semantic Web: there are now useful implementations, and there are workable, if modest, applications of this technology.
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The Semantic Web
Summary: The idea of putting semantic information on the Web has been around for a long time: we now have the beginnings of a practical application. This has its foundations in what is
called Description Logic, which strikes a good balance between tractability and usability. This has led to a Web
language called OWL, which is at the centre of modern work on the Semantic Web: there are now useful implementations, and there are workable, if modest, applications of this technology.
Credits: 15.0
Pre-requisites: XML and Structured Information. Knowedge of logic, its syntax and semantics (up to the first order predicate calculus)
Aims: To show the role and usefulness of semantic information
in the Web
To describe a suitable logical formalism for formalising semantic information
To introduce the technologies of the semantic web, and discuss their strengths and weaknesses
Objectives: i) The reasons for using semantic information on the Web
ii) Introduction to description logic
iii) OWL and other web ontology languages
iv) implementation: Jena and other frameworks
Core Skills: i) Translating common sense knowledge into logical formalism
ii) Programming with knowledge representation
Books: The course textbook is Grigoris Antoniou and Frank van Harmelen,
A Semantic Web Primer,
second edition (MIT Press 2008)
For reference, the main resource will be
the W3C website
http://www.w3.org/2001/sw/
(especially the section on Publications / Articles / Interviews)
Another good reference is
Baader et al. The Description Logic Handbook, 2nd Edition
(Cambridge 2010)
- Machine Learning
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Machine Learning
This course covers methods for machine learning from signals and data, including statistical pattern recognition methods, neural networks, and clustering.
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Machine Learning
Summary:
This course covers methods for machine learning from signals and data, including statistical pattern recognition methods, neural networks, and clustering.
Credits:
15.0
Aims:
The aim of the course is to give students an understanding of machine learning methods, including pattern recognition, clustering and neural networks, and to allow them to apply such methods in a range of areas.
Objectives:
Recall a range of machine learning techniques and algorithms, including neural networks and statistical methods
Use concepts from probability theory in machine learning
Derive and analyse properties of machine learning methods
Discuss the relative merits of different machine learning techniques and approaches
Apply machine learning methods to the analysis of signals and data
Books:
Pattern Recognition and Machine Learning by C. M. Bishop; Springer 2006; ISBN 0387310738
