Descriptif
Signal processing tools are important in many areas of acoustics where experimental or numerical data analysis is required.
This course will give the opportunity for students to learn how to solve simple audio signal processing problems by means of signal models and dictionaries, sparse principles etc.
Objectifs pédagogiques
Being able to
• choose the right signal model: deterministic or random
• choose an appropriate dictionary (Gabor or wavelets) for a given signal and the application
• use the sparse principle for audio signal processing
in order to solve simple audio signal processing problems
Compétences complémentaires :
Use of MATLAB and for programmation and signal processing
- Travaux pratiques : 15
- Cours magistral : 15
effectifs minimal / maximal:
10/30Diplôme(s) concerné(s)
domaines Saclay
Mention Mécanique.Pour les étudiants du diplôme Master 2 Acoustical Engineering
Basics of signal processing (Fourier analysis, Z-transform, FIR and IIR filters, introduction to random signals)
Format des notes
Numérique sur 20Littérale/grade européenPour les étudiants du diplôme Master 2 Acoustical Engineering
Vos modalités d'acquisition :
Exam (70%) + Practical work (30%)
Le rattrapage est autorisé (Note de rattrapage conservée)- le rattrapage est obligatoire si :
- Note initiale < 7
- Crédits ECTS acquis : 3 ECTS
Le coefficient de l'UE est : 3
La note obtenue rentre dans le calcul de votre GPA.
L'UE est évaluée par les étudiants.
Programme détaillé
Theoretical content:
• Reminder on Fourier. Filtering and numerical filters synthesis
• Random signals, Power Spectrum Density (PSD), PSD estimators (periodogram, Welch), AR model
• Frequency estimation (Fourier, HR methods)
• Time-Frequency analysis/synthesis: Short Time Fourier Transform (STFT)/Gabor, bank filters interpretation (Wide/short band, speech analysis), uncertainty principle, non surjectivity of STFT, re-synthesis of the signal (Frames introduction), MDCT
• Non uniform bank filters, introduction to wavelets and non stationary Gabor frames
• Atomic decomposition: Matching Pursuit, Introduction to sparsity
Practical content:
• Introduction to audio Analysis/Synthesis
• Denoising and compression of audio signal
• Audio signal restauration (declipping, decliccing)
