TY - JOUR
T1 - One-parameter fractional linear prediction
AU - Despotovic, Vladimir
AU - Skovranek, Tomas
AU - Peric, Zoran
N1 - Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2018/7
Y1 - 2018/7
N2 - The one-parameter fractional linear prediction (FLP) is presented and the closed-form expressions for the evaluation of FLP coefficients are derived. Contrary to the classical first-order linear prediction (LP) that uses one previous sample and one predictor coefficient, the one-parameter FLP model is derived using the memory of two, three or four samples, while not increasing the number of predictor coefficients. The first-order LP is only a special case of the proposed one-parameter FLP when the order of fractional derivative tends to zero. Based on the numerical experiments using test signals (sine test waves), and real-data signals (speech and electrocardiogram), the hypothesis for estimating the fractional derivative order used in the model is given. The one-parameter FLP outperforms the classical first-order LP in terms of the prediction gain, having comparable performance with the second-order LP, although using one predictor coefficient less.
AB - The one-parameter fractional linear prediction (FLP) is presented and the closed-form expressions for the evaluation of FLP coefficients are derived. Contrary to the classical first-order linear prediction (LP) that uses one previous sample and one predictor coefficient, the one-parameter FLP model is derived using the memory of two, three or four samples, while not increasing the number of predictor coefficients. The first-order LP is only a special case of the proposed one-parameter FLP when the order of fractional derivative tends to zero. Based on the numerical experiments using test signals (sine test waves), and real-data signals (speech and electrocardiogram), the hypothesis for estimating the fractional derivative order used in the model is given. The one-parameter FLP outperforms the classical first-order LP in terms of the prediction gain, having comparable performance with the second-order LP, although using one predictor coefficient less.
KW - Fractional calculus
KW - Fractional derivative
KW - Linear prediction
KW - Optimal prediction
UR - http://www.scopus.com/inward/record.url?scp=85047895495&partnerID=8YFLogxK
U2 - 10.1016/j.compeleceng.2018.05.020
DO - 10.1016/j.compeleceng.2018.05.020
M3 - Article
AN - SCOPUS:85047895495
SN - 0045-7906
VL - 69
SP - 158
EP - 170
JO - Computers and Electrical Engineering
JF - Computers and Electrical Engineering
ER -