Lecture_algorithms_fall_2010_6tp

Lecture_algorithms_fall_2010_6tp - Speech Processing...

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1 Digital Speech Processing Digital Speech Processing- Lecture 14A Algorithms for Speech Processing Speech Processing Algorithms Speech/Non-speech detection – Rule-based method using log energy and zero crossing rate – Single speech interval in background noise Voiced/Unvoiced/Background classification – Bayesian approach using 5 speech parameters – Needs to be trained (mainly to establish statistics for background signals) Pitch detection Estimation of pitch period (or pitch frequency) during regions of voiced Estimation of pitch period (or pitch frequency) during regions of voiced speech – Implicitly needs classification of signal as voiced speech – Algorithms in time domain, frequency domain, cepstral domain, or using LPC-based processing methods Formant estimation – Estimation of the frequencies of the major resonances during voiced speech regions – Implicitly needs classification of signal as voiced speech – Need to handle birth and death processes as formants appear and disappear depending on spectral intensity Median Smoothing and Speech Processing Why Median Smoothing Obvious pitch period discontinuities that need to be smoothed in a manner that preserves the character of the surrounding regions – using a median (rather than a linear filter) smoother. Running Medians 5 point median 5 point averaging Non Non-Linear Smoothing Linear Smoothing linear smoothers (filters) are not always appropriate for smoothing parameter estimates because of smearing and blurring discontinuities pitch period smoothing would emphasize errors and distort the contour use combination of non-linear smoother of running medians and linear smoothing linear smoothing => separation of signals based on non-overlapping 6 linear smoothing => separation of signals based on non overlapping frequency content non-linear smoothing => separating signals based on their character (smooth or noise-like) 1 [ ] ( [ ]) ( [ ]) - smooth rough components ([] ) m e d i a n ) ) ( [ ]) median of [ ]. .. [ ] = ++ == =− + L L xn Sxn Rxn yxn M xn Mxn xn xnL
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2 Properties of Running Medians Running medians of length L : 1. M L ( α x [ n ]) = α M L ( x [ n ]) 2. Medians will not smear out discontinuities (jumps) in the signal if there are no 7 (jumps) in the signal if there are no discontinuities within L/2 samples 3. M L ( α x 1 [ n ] + β x 2 [ n ]) α M L ( x 1 [ n ]) + β M L (x 2 [ n ] ) 4. Median smoothers generally preserve sharp discontinuities in signal, but fail to adequately smooth noise-like components Median Smoothing 8 Median Smoothing 9 Median Smoothing 10 Median Smoothing 11 Nonlinear Smoother Based on Medians 12
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3 Nonlinear Smoother - [ ] is an approximation to the signal ( [ ]) - second pass of non-linear smoothing improves performance based on: [ ] ( [ ]) - the difference signal, [ ], is formed as: = yn Sxn yn Sxn zn 13 [ ] [ ] [ ] ( =−= xn R [] ) - second pass of nonlinear smoothing of [ ] yields a correction term that is added to [ ] to give [ ], a refined approximation to ( [ ]) [ ] ( [ ]) [ ( [ ])] - if [ ] ( [ ]) exactly, =+ = wn SRxn Rxn i.e., the non-linear smoother was ideal, then [ ( [ ])] would be identically zero and the correction term would be unnecessary
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Lecture_algorithms_fall_2010_6tp - Speech Processing...

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