1. linear predictive coding (LPC)
an application driven approach
adapted from guest lecture for mobile application development for sensing and control, EE596
Friday, August 30, 13

2. non-parametric parametric
Use Data or Transform Fit Data to a Model
Data
Derivative
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3. non-parametric parametric
Use Data or Transform Fit Data to a Model
Data
Derivative
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4. non-parametric parametric
Use Data or Transform Fit Data to a Model
Data
Derivative
derivative[n] = y[n]-y[n-1]
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5. non-parametric parametric
Use Data or Transform Fit Data to a Model
Data
Derivative
derivative[n] = y[n]-y[n-1]
Friday, August 30, 13

6. non-parametric parametric
Use Data or Transform Fit Data to a Model
Data
Derivative
derivative[n] = y[n]-y[n-1]
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7. non-parametric parametric
Use Data or Transform Fit Data to a Model
Data
Derivative
cos(x)
derivative[n] = y[n]-y[n-1]
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8. non-parametric parametric
Use Data or Transform Fit Data to a Model
Data
Derivative
cos(x)
derivative[n] = y[n]-y[n-1]
-sin(x)
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9. the tradeoff of parametric modeling
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10. the tradeoff of parametric modeling
- need to fit a model to the data
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11. the tradeoff of parametric modeling
- need to fit a model to the data
+ (might be) easier to manipulate model
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12. non-parametric parametric
Use Data or Transform Fit Data to a Model
two signals = 1500 Hz and 5500 Hz two signals = 1500 Hz and 5500 Hz
Magnitude
freq (kHz) freq (kHz)
Magnitude
Friday, August 30, 13

13. non-parametric parametric
Use Data or Transform Fit Data to a Model
two signals = 1500 Hz and 5500 Hz
FFT, array
two signals = 1500 Hz and 5500 Hz
Magnitude
freq (kHz) freq (kHz)
Magnitude
Friday, August 30, 13

14. non-parametric parametric
Use Data or Transform Fit Data to a Model
two signals = 1500 Hz and 5500 Hz
FFT, array
two signals = 1500 Hz and 5500 Hz
Magnitude
freq (kHz)
LPC polynomial
freq (kHz)
Magnitude
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15. what model should we fit to?
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16. what model should we fit to?
a filter with feedback
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17. what model should we fit to?
a filter with feedback
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18. what model should we fit to?
a filter with feedback
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19. feedback filters are system models
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20. feedback filters are system models
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21. feedback filters are system models
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22. feedback filtering
a
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23. feedback filtering
want to estimate a
a
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24. feedback filtering
what can we represent with this equation?
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25. ak
k k k
feedback filtering
what can we represent with this equation?
3
2
1
0
-1
-2
-3
1 3 5 7 9 11 13
3
2
1
0
-1
-2
-3
1 3 5 7 9 11 13
3
2
1
0
-1
-2
-3
1 3 5 7 9 11 13
piano marimba violin
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26. feedback filter equation in frequency
…
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27. feedback filter equation in frequency
…
Y (z) =
E(z)
1
Pp
k=1 akz k
z = ej!
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28. is this a good model for frequency analysis?
Y (z) =
1
1
Pp
k=1 akz k
E(z)
Y (z) =
1
Qp
k=1(1 rkz 1)
E(z)
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29. is this a good model for frequency analysis?
resonant frequency = complex angle of root
resonance bandwidth = related to magnitude of root
Y (z) =
1
1
Pp
k=1 akz k
E(z)
Y (z) =
1
Qp
k=1(1 rkz 1)
E(z)
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30. examples
Y (z) =
1
Qp
k=1(1 rkz 1)
E(z)
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31. another interpretation, vocal tract
sourcefilter
Y (z) =
1
1
Pp
k=1 akz k
E(z)
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32. another interpretation, vocal tract
sourcefilter
Y (z) =
1
1
Pp
k=1 akz k
E(z)
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33. another interpretation, vocal tract
sourcefilter
Y (z) =
1
1
Pp
k=1 akz k
E(z)
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34. another interpretation, prediction
…
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35. another interpretation, prediction
…
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36. 17
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37. 18
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38. 18
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39. 18
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40. summary of interpretations
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41. summary of interpretations
Spectral Estimation == Auto Regressive
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42. summary of interpretations
Spectral Estimation == Auto Regressive
Forecasting == Linear Prediction
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43. summary of interpretations
Spectral Estimation == Auto Regressive
Forecasting == Linear Prediction
Vocal Tract Model == Source/Filter
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44. common applications
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45. common applications
Speech Vocoders
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46. common applications
Speech Vocoders
Spectral Analysis
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47. common applications
Speech Vocoders
Spectral Analysis
Pitch Estimation
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48. common applications
Speech Vocoders
Spectral Analysis
Pitch Estimation
Voice Changers
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49. common applications
Speech Vocoders
Spectral Analysis
Pitch Estimation
Voice Changers
Friday, August 30, 13

50. common applications
Speech Vocoders
Spectral Analysis
Pitch Estimation
Voice Changers
Analysis/Synthesis of Instrument Sounds
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51. common applications
Speech Vocoders
Spectral Analysis
Pitch Estimation
Voice Changers
Analysis/Synthesis of Instrument Sounds
Voice Box
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52. common applications
Speech Vocoders
Spectral Analysis
Pitch Estimation
Voice Changers
Analysis/Synthesis of Instrument Sounds
Voice Box
Compression (i.e., mpeg4, CELP)
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53. common applications
Speech Vocoders
Spectral Analysis
Pitch Estimation
Voice Changers
Analysis/Synthesis of Instrument Sounds
Voice Box
Compression (i.e., mpeg4, CELP)
My research– medical sensing from a microphone
Friday, August 30, 13

54. questions?
Topics Related to LPC and Further Reading:
LPC10, Ultra Low Bit Rate Voice Coding
Code Excited Linear Prediction
Levinson-Durbin Recursion
Burg’s Method
LP Cepstral Coefficients
The Talking Orchestra
SpiroSmart, the mobile phone spirometer
eclarson.com
eclarson@uw.edu
@ec_larson
electrical
engineering
computer
science
Friday, August 30, 13