Dansk - English
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Digital signal processing and mathematics 4 (Spring 2007) |
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| Course code : | EDSM4-U01 | ||
| ECTS Credits : | 10 | Status : | Compulsory |
| Revised : | 14/02 2006 | Written : | 06/12 2001 |
| Placement : | 4. semester | Hours per week : | 8 |
| Length : | 1 semester | Teaching Language : | English if English students are present |
| Objective : | The study module should make the student able to: Analyse discrete-time signals and systems in the frequency domain. This includes the understanding and evaluation of the problems and limitations of computer-based frequency analysis of analog signals. Understand elementary definitions, concepts and calculation methods concerning probability and statistics and to apply these in the description of stochastic signals. Evaluate the possibility for data compression of a signal source on basis of the source’s statistical properties. Explain the fundamental principles for speech coding and evaluate different standards. Explain the principles of automatic speech recognition and to produce a single word speech recognizer Work out, elaborate and defend the documentation of the compulsory group assignments of the course. |
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| Principal Content : | The main content is divided into three parts: A, B, C and D: A. Discrete time Fourier analysis: Discrete time Fourier transform (DTFT) and inverse (IDTFT). Discrete Fourier transform (DFT/FFT) and inverse (IDFT/IFFT). Practical spectral analysis: Spectrum analysers, frequency resolution, amplitude precision, window functions, leakage, zero padding. B. Probability and signals: Probability: definitions, calculus and Probability functions. Stochastic Signals. Elementary Information Theory and Source Coding. C. Speech Coding in telecommunication: Quantization noise, linear Pulse Code Modulation (PCM) and companding. Differential PCM (DPCM) and linear prediction, adaptive DPCM (ADPCM), Pitch-excited linear predictictive coding (LPC). Modern speech coding principles and standards (GSM etc). D. Automatic Speech Recognition: Cepstrum Analysis, Feature Extraction, Time Warping, Model word production, Distance computation and word recognition. |
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| Teaching method : | In the beginning of the semester the teaching will be a mixture of classroom lecturing and problem work solving, partly based on MATLAB as the simulation tool. This form makes up 50% to 75% of the scheduled time in the semester, distributed as 100% in the beginning of the semester and 0% towards the end of the semester. In relation to the contents items A and B two large course assignments have to be carried out and documented in writing. In relation to the contents item C and D a large course assignment involving design of a speech recognizer has to be carried out and documented in a report. | ||
| Required prequisites : | Documented knowledge similar to DSM2 | ||
| Recommended prerequisites : | - | ||
| Relations : | Prerequisite for the elective study modules: DCEC, DCM, WWL, DIP, LAAF, ANN. | ||
| Type of examination : | Look under remarks | ||
| External examiner : | External | ||
| Marking : | Scale of 13 | ||
| Remarks : | Oral examination based on assignments with individual grading. Before the examination: A presentation by each group member of a self-chosen topic from the item C/D course assignment in prescence of the entire group. It is required that the topic selection is coordinated between group members to be non-overlapping and cover major points of the item C and D assignment with professional width and depth. Approximately 10 minutes per student is available for this presentation. Examination: Internal and external examiner ask supplementary questions to the presentation. After this the students are examined in a topic, drawn at random from a pool of topics originating from the course assignments and the teaching as a whole. Approximately 10 minutes per student is available for this examination. The grade is given on the basis of the quality of the report of the item C/D assignment and the presentation/defence of the self-chosen topic (50%) and the individual examination in the randomly drawn topic (50%). |
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| Teaching material : | James McClellan, Ronald Schafer, Mark Yoder: Signal Processing First. (Prentice Hall 2003). ISBN 0-13120265-0. Croft, Davison, Hargreaves: Engineering Mathematics (Addisson Wesley) Electronic notes available on the Campusnet fileshare and printed handouts. |
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| Responsible teacher : | Anne Marie Hinke
, amh@ihk.dk |
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