Code
IGSE SIC 7001
Semester
Spring
Domain
Signal et Communications
Program
Master
Language
Anglais/English
ECTS Credits
6
Class hours
24
Workload
96
Program Manager(s)
Department
- Electronique et Physique
Educational team
Organisation
Cours/TD/TP/projet/examen : 72Learning objectives
- To remind basic notions in signal processing, namely the transformation from the analog domain towards the digital domain (sampling/quantization) and frequency-domain analysis methods
- Introduction to some advanced signal enhancement techniques for noise-embedded measurements contexts in order to improve the output sensors signal-to-noise ratio (SNR): instances of some kinds of signals will be presented (speech, biomedical signals, biometric signals…)
- To introduce time-frequency analysis methods often used for the signal enhancement (in transmission) and extraction of its relevant parameters (after A-D conversion) in Pattern Recognition, in order to improve the global system robustness to adverse environments (interferences).
- N.B.: here we mean by « signal enhancement » (cf. title) all the approaches aiming at analysing in a relevant way (extraction) and/or to reduce noise signal before its analysis.
Prerequisites
- Basic knowledge in Fourier Analysis and in Statistics
Keywords
Sampling, quantization, time-frequency analysis, parameters extraction, linear and adaptive filtering, noise reduction, multi-cadence filtering.
Content
A) Characterisation of different signals (recalls): deterministic/stochastic signals, notion of noise signals, Autocorrelation functions and Spectral Energy/Power Densities.
B) Linear Filtering of Signals: convolution, invariant linear filters, filter transfer functions (in Z and frequency domains).
C) Signal acquisition : analog-to-digital conversion, Sampling principle, quantization (SNR ratio).
D) Digital linear filtering
E) Time-frequency analysis : Discrete & Fast Fourier Transforms (DFT & FFT), Wavelets Transforms, Homomorphic Analysis (Cepstrum, LFCC , MFCC, Root-spectral…).
F) Noise reduction : Wiener filter, Spectral Substraction , Adaptive filtering.
G) Multi-cadence filtering : Filterbanks, complexity reduction
Evaluation
1st session = control (C1)
2nd session = control (C2)
Final mark = Sup (C1,C2)