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Perceptual declipping of audio signals through compressed sensing: algorithm design and evaluation Tussentijdse presentatie Naim MansourPromotor: Prof.

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Presentation on theme: "Perceptual declipping of audio signals through compressed sensing: algorithm design and evaluation Tussentijdse presentatie Naim MansourPromotor: Prof."— Presentation transcript:

1 Perceptual declipping of audio signals through compressed sensing: algorithm design and evaluation Tussentijdse presentatie Naim MansourPromotor: Prof. dr. ir. Marc Moonen Assistent: Ir. Bruno Defraene

2 Overzicht Onderwerp & doelstellingen (vermelding Steven) – 3 min. Compressed sensing – 5 min. – Wat? – Theoretisch – Declipping(don’t forget perfect reconstruction) CS & Declipping – 4 min. – Specifieke theorie – Eerder werk (INRIA, AxBe) – Kort: perceptuele component Toelichting gemaakte keuzes & motivatie (2 keuzes) – 3 min. – Don’t forget frame length (basically all details) Implementatie & resultaten (demo) – 5 min. Planning, en plannen voor fase 2 – 2 min. Dank & vragen 2

3 Overview Subject Compressed Sensing CS & Declipping Perceptual components Extra: IRL1 Implementation Evaluation 3

4 Subject Declipping of audio signals Through compressed sensing Perceptual Algorithm design & evaluation 4

5 Compressed Sensing: general 5

6 Solution equals translation of null(A)-plane by vector z L 0 & L 1 lead to sparse solutions, L 2 doesn’t L 1 minimization is convex -> convex optimization, L 0 minimization non-convex -> greedy opt. 6 Compressed Sensing: Choice of L p

7 Compressed Sensing: AxBe model 7

8 Compressed Sensing: Recovery 8

9 CS & Declipping: recovery 9 M n x (max)8,66994,64593,32812,72022,29532,13931,89

10 10

11 CS & Declipping: previous work INRIA Bölcskei 11

12 Perceptual components Perceptual weighting matrix based on acoustic loudness perception Psychoacoustically optimized (adaptive) basis 12

13 Extra: IRL1 Iteratively reweighted L1 minimization (Candès, Wakin, Boyd – 2007) 13

14 Implementation: general 2 main choices – PCS through bounded L1 minimization, using perceptual weighting, Axy & AxBe models (further improvement through IRL1) – PCS through bounded L0 minimization, using psychoacoustic wavelet basis, Axy & AxBe models Incremental design: implementation & evaluation with & without bounds, with & without perceptual components,… 14

15 15

16 Implementation: Clipping 16

17 Evaluation: general 17

18 Evaluation: SNR vs. PEAQ 18

19 SNR no guarantee for audio quality!! 19

20 Planning & future prospects 20

21 Planning & future prospects 21

22 Planning & future prospects Semester 2 – Execute psychoacoustic experiments – Finish algorithms – Write final texts 22

23 References rials/ssp07-cs-tutorial.pdf rials/ssp07-cs-tutorial.pdf Recovery of Sparsely Corrupted Signals blablabla 23

24 ? 24

25 Zalig Kerstfeest! 25


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