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- W2892100568 abstract "Cost-effective adaptive system identification is a challenging problem in speech processing especially when the acoustic impulse response is “long”. In this paper, an overview of three of the mostly-used recent sparse adaptive filtering algorithms is presented; and their performances in the context of system identification are studied and compared. The algorithms of interest include the proportionate normalized least mean square (PNLMS), its sparseness-controlled (SC) upgrade (SC-PNLMS) as well as the so-called variable-step-size zero-attractor NLMS (VSS-ZA-NLMS) which is based on the compressive sensing (CS) framework. Series of simulations were carried out both in synthetic and real different-sparseness long acoustic impulse responses with stationary and non-stationary inputs in order to effectively analyze, evaluate and compare the strengths and the weaknesses of these algorithms in terms of convergence speed, steady-state performance and computational complexity." @default.
- W2892100568 created "2018-09-27" @default.
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- W2892100568 date "2018-09-11" @default.
- W2892100568 modified "2023-10-14" @default.
- W2892100568 title "Compressive Sensing Based and PNLMS-Type Sparse Adaptive Filtering Algorithms for the Identification of Long Acoustic Impulse Responses" @default.
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- W2892100568 doi "https://doi.org/10.1007/978-3-319-97816-1_39" @default.
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