Lecture 17_winter_2012

Lecture 17_winter_2012 - Digital Speech Processing Lecture...

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1 Digital Speech Processing— Lecture 17 Speech Coding Methods Based on Speech Models
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2 Waveform Coding versus Block Processing • Waveform coding – sample-by-sample matching of waveforms – coding quality measured using SNR • Source modeling (block processing) – block processing of signal => vector of outputs every block – overlapped blocks Block 1 Block 2 Block 3
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3 Model-Based Speech Coding we’ve carried waveform coding based on optimizing and maximizing SNR about as far as possible – achieved bit rate reductions on the order of 4:1 (i.e., from 128 Kbps PCM to 32 Kbps ADPCM) at the same time achieving toll quality SNR for telephone-bandwidth speech to lower bit rate further without reducing speech quality, we need to exploit features of the speech production model, including: – source modeling – spectrum modeling – use of codebook methods for coding efficiency we also need a new way of comparing performance of different waveform and model-based coding methods – an objective measure, like SNR , isn’t an appropriate measure for model- based coders since they operate on blocks of speech and don’t follow the waveform on a sample-by-sample basis – new subjective measures need to be used that measure user-perceived quality, intelligibility, and robustness to multiple factors
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4 Topics Covered in this Lecture • Enhancements for ADPCM Coders – pitch prediction – noise shaping • Analysis-by-Synthesis Speech Coders – multipulse linear prediction coder (MPLPC) – code-excited linear prediction (CELP) • Open-Loop Speech Coders – two-state excitation model – LPC vocoder – residual-excited linear predictive coder – mixed excitation systems • speech coding quality measures - MOS • speech coding standards
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5 ] [ n x ] [ n d ] [ ˆ n d ] [ n c ] [ ~ n x ] [ ˆ n x Differential Quantization = = p k k z z P 1 ) ( ] [ n c ] [ ˆ n d ] [ ˆ n x ] [ ~ n x P : simple predictor of vocal tract response
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6 Issues with Differential Quantization • difference signal retains the character of the excitation signal – switches back and forth between quasi- periodic and noise-like signals • prediction duration (even when using p =20) is order of 2.5 msec (for sampling rate of 8 kHz) – predictor is predicting vocal tract response – not the excitation period (for voiced sounds) • Solution – incorporate two stages of prediction, namely a short-time predictor for the vocal tract response and a long- time predictor for pitch period
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7 Pitch Prediction • first stage pitch predictor: • second stage linear predictor (vocal tract predictor): 1 () β = M Pz z 2 1 α = = p k k k z residual [] xn ˆ + dn + % + + + ˆ Transmitter Receiver + + + + + + + + + ) ( 1 z P ) ( 2 z P ) ( 1 1 z P = = = p k k k M z z P z z P 1 2 1 ) ( ) ( ] [ Q ) ( 1 z P ) ( 2 z P ˆ ) ( z P c
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8 Pitch Prediction 1 () first stage pitch predictor: this predictor model assumes that the pitch period, , is an integer number of samples and is a gain constant allowing for variations in pitc β =⋅ ± ± M Pz z M 1 11 2 3 h period over time (for unvoiced or background
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Lecture 17_winter_2012 - Digital Speech Processing Lecture...

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