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Understanding linear predictive part of LPC speech compression


Sinay
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Hi I'm taking a multimedia systems course and I'm preparing for my exam on tuesday. I'm trying to get my head around LPC compression on a general level, but I'm having trouble with what is going on with the linear predictive filter part. This is my understanding so far:

 

LPC works by digitising the analog signal and splitting it into segments. For each of the segments we determine the key features of the signal and try to encode these as accurately as possible. The key features are the pitch of the signal (i.e. the basic formant frequency), the loudness of the signal and whether the sound is voiced or unvoiced. Parameters called vocal tract excitation parameters are also determined which are used in the vocal tract model to better model the state of the vocal tract which generated the sound. This data is passed over the network and decoded at the receiver. The pitch of the signal is used as input to either a voiced or unvoiced synthesiser, and the loudness data is used to boost the amplitude of this resulting signal. Finally the vocal cord model filters this sound by applying the LPC coefficients which were sent over the network.

 

In my notes it says that the vocal tract model uses a linear predictive filter and that the nth sample is a linear combination of the previous p samples plus an error term, which comes from the synthesiser.

 

  • does this mean that we keep a running average of last p samples at both the encoder and decoder? So that at the encoder we only transmit data that corresponds to the difference between this average and actual signal?
  • Why is it an a linear combination of these previous samples? My understanding is that we extract the loudness, frequency and voiced/unvoiced nature of the sound and then generate these vocal tract excitation parameters by choosing them so that the difference between the actual signal and the predicted signal is as small as possible. Surely an AVERAGE of these previous samples would be a better indication of the next sample?

If there are any holes in my understanding if you could point them out that would be great! Thanks in advance!

 

 

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