SubjectsSubjects(version: 873)
Course, academic year 2020/2021
Digital Sound Processing - NPFL109
Title: Číslicové zpracování zvukových signálů
Guaranteed by: Institute of Formal and Applied Linguistics (32-UFAL)
Faculty: Faculty of Mathematics and Physics
Actual: from 2019
Semester: winter
E-Credits: 6
Hours per week, examination: winter s.:2/2 C+Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: Czech
Teaching methods: full-time
Guarantor: Mgr. David Klusáček, Ph.D.
Class: Informatika Mgr. - volitelný
Classification: Informatics > Computer and Formal Linguistics
Annotation -
Last update: G_I (30.05.2013)
Introductory course in one-dimensional (mostly sound) signal processing. Complements lectures NPFL079 (Algorithms in Speech Recognition) but can be taken separately as well. The lectures cover the theory of digital filters, FFT and its application in implementing fast convolution, sampling theorem, time-frequency signal representation and its connection with overcomplete representation via frame of the respective vector space, deconvolution and signal restoration.
Course completion requirements -
Last update: Mgr. David Klusáček, Ph.D. (29.10.2019)

To successfully finish the course, a student is required to attend the exercises (could miss up to 3 of them -- more is possible for extra homeworks) and pass the oral exam.

Literature -
Last update: G_I (30.05.2013)

[1] R.W.Hamming. Digital Filters. Prentice-Hall, New Jersey, 1977

[2] Jiří Jan: Číslicová filtrace a restaurace signálů, VUTIUM, 2002

Requirements to the exam - Czech
Last update: Mgr. David Klusáček, Ph.D. (19.06.2019)

Zkouska probiha ustne formou diskuse nad resenim zadaneho problemu, ktery lze vyresit s pouzitim nastroju uvedenych v syllabu.

Syllabus -
Last update: G_I (30.05.2013)

(1) Discrete periodic signals. Discrete Fourier Transform and its properties (Parseval's theorem, convolution theorem).

(2) FFT algorithm, fast multiplication of polynomials, fast convolution.

(3) Fourier series and discrete non-periodic signals.

(4) Operations with signals (modulation, convolution, non-linear distortion).

(5) Linear Time-Invariant systems. Digital filters and their general form. IIR and FIR parts. Theorem on existence and uniqueness of digital filter solution. Invertibility, two-way filters.

(6) Bode plot. Magnitude and phase. Phase delay, group delay, wave delay.

(7) Implementing IIR filters (canonic forms). Round-off errors, stability and noise.

(8) Minimum phase filters. Magnitude vs. group delay theorem.

(9) Filter design methods.

(10) Hilbert transform, analytic signal.

(11) Sampling theorem, aliasing. Band-limited signals, Gibbs phenomenon. Resampling. A/D and D/A converters. Kell phenomenon.

(12) Uncertainty principle and time-frequency representation.

(13) Linear prediction (LPC). ASR front-ends.

(14) Deconvolution, Wiener filter. Blind deconvolution. Echo suppression by temporal masking.

(15) Frame of the vector space. Reconstruction theorem.

(16) Signal restoration (denoising).

(17) Biological sound processing: Human auditory system and pathways.

Exercises will have a form of practical application examples (e.g. equalizer, speaker location, principle of active and passive radar (sonar), signal restoration (denoising), etc.). These would be selected so as to exercise the theory just learned.

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