OVERVIEW OF SOME POPULAR CODECS in .NET

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OVERVIEW OF SOME POPULAR CODECS
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y(n) Framer LSP quantizer Simulated decoder LSP decoder LSP interpolator
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~ Am(z) Wm(z) Impulse response calculator Pm(z) Hm(z) Memory update eu,m(n) Pitch prediction decoder Lopt,m, bopt,m em(n) ev,m(n)
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LPC analysis
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Formant perceptual weighting fm(n) Pitch estimator
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Figure 11.5 Block diagram of G.723.1 CODEC
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Calculating the LPC coef cients requires the past, current, and future subframes. The LPC synthesis lter is de ned as 1 = Am (z) 1 1
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10 i=1
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(11.21)
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ai,m z i
where m = 0, 1, 2, 3 is the subframe index. As shown in Figure 11.5, the LPC lter coef cients of the last subframe are converted to LSP (line spectral pairs) coef cients. The reason for doing LPC to
180-sample LPC analysis windows
Past 60
Subframe1
Subframe2 Subframe3 240
Subframe4
Look-ahead 60
G.723.1 LPC analysis windows vs. subframes
Table 11.4 Seq. 1 2 3 4 5 Computed parameters
SPEECH-CODING TECHNIQUES
Procedures of LPC coef cient calculation and reconstruction Subframe 0 {ai,0 } Subframe 1 {ai,1 } Subframe 2 {ai,2 } Subframe 3 {ai,3 } { i,3 } { i,3 } { i,3 } {ai,3 }
LPC coef cients LSP coef cients LSP coef cients quantization and dequantization Interpolated LSP coef cients Reconstructed LPC coef cients
{ i,0 } {ai,0 }
{ i,1 } {ai,1 }
{ i,2 } {ai,2 }
LSP coef cients conversion is to take the advantages of two properties of LSP coef cients: to verify the stability of the lter and to have higher coef cients correlation among subframes. The rst property can be used to make synthesis lter stable after quantization, and the second is used to further remove redundancy. The LPC coef cients calculation and quantization can be further explained with Table 11.4 and the following procedures:
1. Compute {ai,m } for subframes m = 0, 1, 2, 3 and 10th-order LPC coef cients i = 1, . . . , 10. With {ai,m }, we can construct Equation (11.21). The unquantized LPC coef cients are used to construct the short-term perceptual weighting lter Wm (z), which will be used to lter the entire frame to obtain the perceptually weighted speech signal. 2. Convert the last subframe s {ai,3 } to LSP coef cients { i,3 }. 3. Use vector quantization to quantize the 10 LSP coef cients into LSP index for transmitting. Dequan tize the LSP coef cients to { i,3 }. 4. Use the LSP coef cients { i,3 } from the current frame and the last frame to interpolate the LSP coef cients for each subframe { i,m }. 5. Convert LSP coef cients { i,m } back to LPC coef cients {ai,m } and construct the synthesis lter m (z) for each subframe. Note that even in the encoder side, we also need this in order for both 1/ A sides to use the same set of synthesis lter. Decoding side never has the unquantized LPC coef cients.
The pitch estimation is performed on two adjacent subframes of 120 samples. The pitch period is searched in the range from 18 to 142 samples. Using the estimated open-loop pitch period L olp,m , a harmonic noise-shaping lter Pm (z) can be constructed. The combination of the LPC synthesis lter with the formant perceptual weighting lter and the harmonic noise-shaping lter, Hm (z) = Wm (z) Pm (z) / Am (z), is used to create an impulse response h m (n)(n = 0, . . . , 59). The adaptive excitation signals ev,m (n) and the secondary excitation signal eu,m (n) are ltered by this combined lter to provide the zero-state responses u m (n) and vm (n), respectively. In adaptive codebook excitation, a fth-order pitch predictor is used. The optimum pitch periods (L opt,m ) of subframe 0 and 2 are computed via closed-loop vector quantization around the open-loop pitch estimate L olp,m . The optimum pitch periods in subframes 1 and 3 are searched for differential values around the previous optimum pitch periods of subframes 0 and 2, respectively. The pitch periods of subframes 0 and 2, and the differential values for subframes 1 and 3 are transmitted to the decoder.