Digital Signal Processing Chapter 7: Parametric Spectrum Estimation
Digital Signal Processing Chapter 7: Parametric Spectrum Estimation Digital Signal Processing Chapter 7: Parametric Spectrum Estimation
example 5:comparison parametric ←→ traditionalLDS in dB →0-5-10-15-20-25-30Burg-Algorithma) Burg-Algorithmus2σ-Grenzeideal (-)Mittelw.(--)-35-40-45-500 0.5 1 1.5 2 2.5 3 3.5 4f in kHz →Yule-Walker (with/without window function)LDS in dB →0-5-10-15-20-25-30ohneFensterb) Yule-Walker-Methode2σ-Grenzeideal (-)Mittelw.(--)-35-40-45-500 0.5 1 1.5 2 2.5 3 3.5 4f in kHz →• Monte-Carlo simulation ⇒ bias and scattering (2σ(95%)-border)• Burg-Method and Yule-Walker-estimation with window function comparable• Yule-Walker-estimation without window function: bad unbiasedness (bias!)application for speech coding Page 42
- Page 1 and 2: transparencies - lecture: Digital S
- Page 3 and 4: 7.2 Markov Process as an Example fo
- Page 5 and 6: Past values x(k − 1), · · · ,x
- Page 7 and 8: Conjugating all elements yields inI
- Page 9 and 10: 7.4.2 The Principle of Orthogonalit
- Page 11 and 12: 7.4 Linear Prediction (Overview)app
- Page 13 and 14: 7.5 Levinson-Durbin Recursion∑A r
- Page 15 and 16: ecursion for the predictive coeffic
- Page 17 and 18: Levinson-Durbin Recursion• initia
- Page 19 and 20: 7.6 The Lattice-Structure7.6.1 Anal
- Page 21 and 22: Lattice Structure:• (r + 1)th sta
- Page 23 and 24: 7.6.3 Minimal Phase - Stability•
- Page 25 and 26: Br B q (0) = σ2 ∑q+1rγ rν=1a q
- Page 30 and 31: 2. Covariance Method• disadvantag
- Page 32 and 33: N−1∑k=rN−1∂ ∑N−1∑[e r
- Page 34 and 35: • rth iteration:⎡⎢⎣1â r,1
- Page 36 and 37: 7.8 Examples for Parametric Spectru
- Page 38 and 39: • application for speech codingso
- Page 40 and 41: example 3:vowel ”a”, german mal
example 5:comparison parametric ←→ traditionalLDS in dB →0-5-10-15-20-25-30Burg-Algorithma) Burg-Algorithmus2σ-Grenzeideal (-)Mittelw.(--)-35-40-45-500 0.5 1 1.5 2 2.5 3 3.5 4f in kHz →Yule-Walker (with/without window function)LDS in dB →0-5-10-15-20-25-30ohneFensterb) Yule-Walker-Methode2σ-Grenzeideal (-)Mittelw.(--)-35-40-45-500 0.5 1 1.5 2 2.5 3 3.5 4f in kHz →• Monte-Carlo simulation ⇒ bias and scattering (2σ(95%)-border)• Burg-Method and Yule-Walker-estimation with window function comparable• Yule-Walker-estimation without window function: bad unbiasedness (bias!)application for speech coding Page 42
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