用电信真麻烦阿。。。

只好把文档发在这里了。。。attention:if met "insert pic i",please insert the corresponding ith picture;

As an instruction in summary,I want ot show you that wrls-vff is adaptive weighted recursive least squares algorithm with a variable forgetting factor for short.
It aims to adjust the size of the data segment to be analyzed according to its time-varying characteristics,as during the during the transitions between vowels and consonants.
This algorithm can accurately estimate the vocal tract formants,anti-formants,and their bandwidths,be used for glottal inverse flitering,perform voiced (V)/unvoiced(U)/silent(S) classification of speech segments,estimate the input excitation(either white noise or periodic pulse trains),and estimate the instant of glottal closure.
After the instruction in concise,we will see entire description of this algorithm with input estimation in a stable version.First of all,we need to assume that the speech signal is generated by an autoregressive,moving average(ARMA) model,as the following equation:
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where yk denotes the kth sample of the speech signal,uk is the input excitation,(p,q) are the order of the poles and zeros,respectively,and ai(k) and bj(k) are the time-varying AR and MA parameters,respectively.
The next,we need to define a parameter vector θk and a data vector φk, by the following equations:
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With these two assumptions,we can easily define our whole wrls-vff algorithm with input estimation as follow:
Prediction error:
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Gain update:
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Forgetting factor:
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Input estimate:
a)Pulse input
if λk<λ0 then
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b)White noise input
if λk>λ0 then
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Parameter update:
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Covariance matrix:
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