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Simplified fast least-squares algorithms for speech predictors

Simplified fast least-squares algorithms for speech predictors

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The letter deals with the development of a simplified fast least-squares algorithm which is free of roundoff error accumulation. The simplified algorithm requires 9N MADPR (multiplications and divisions per recursion) rather than 10N MADPR as in the fast least squares or fast Kalman (FLS) case where N is the order of predictor. The superiority of SFLS over FLS and LMS approaches is illustrated by prediction gain performance curves for various speech signals.

References

    1. 1)
      • G. Carayannis , D. Monolakis , N. Kalouptsidis . A fast algorithm for least squares filtering and prediction. IEEE Trans. , 1394 - 1402
    2. 2)
      • J. Cioffi , T. Kailath . Fast recursive LS transversal filters for adaptive filtering. IEEE Trans. , 304 - 337
    3. 3)
      • M.G. Bellanger . (1987) , Adaptive digital filtering and signal analysis.
    4. 4)
      • Fabre, P., Gueguen, C.: `Fast recursive LS algorithms preventing divergence', Proc. ICASSP, 1985, p. 1149–1152.
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