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Lecture 9_winter_2012_6tp

Lecture 9_winter_2012_6tp - General Discrete-Time Model of...

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1 1 Digital Speech Processing— Lecture 9 Short-Time Fourier Analysis Methods- Introduction 2 General Discrete-Time Model of Speech Production Voiced Speech: A V P(z)G(z)V(z)R(z) Unvoiced Speech: A N N(z)V(z)R(z) 3 Short-Time Fourier Analysis represent signal by sum of sinusoids or complex exponentials as it leads to convenient solutions to problems (formant estimation, pitch period estimation, analysis-by-synthesis methods), and insight into the signal itself • such Fourier representations provide – convenient means to determine response to a sum of sinusoids for linear systems – clear evidence of signal properties that are obscured in the original signal 4 Why STFT for Speech Signals steady state sounds, like vowels, are produced by periodic excitation of a linear system => speech spectrum is the product of the excitation spectrum and the vocal tract frequency response speech is a time-varying signal => need more sophisticated analysis to reflect time varying properties – changes occur at syllabic rates (~10 times/sec) – over fixed time intervals of 10-30 msec, properties of most speech signals are relatively constant (when is this not the case) 5 Overview of Lecture • define time-varying Fourier transform ( STFT ) analysis method • define synthesis method from time-varying FT (filter-bank summation, overlap addition) show how time-varying FT can be viewed in terms of a bank of filters model computation methods based on using FFT application to vocoders, spectrum displays, format estimation, pitch period estimation 6 Frequency Domain Processing Coding : – transform, subband, homomorphic, channel vocoders Restoration/Enhancement/Modification : – noise and reverberation removal, helium restoration, time-scale modifications (speed-up and slow-down of speech)

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