COI code: ISCEE09_036
Paper Language: English
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Authors Application of Classical Adaptive Filters in Speech EnhancementMohammad Shams Esfand Abadi - Department of Electrical Engineering, Tarbiat Modares University, Tehran, Iran، Department of Electrical Engineering, Shahid Rajaee Teachers Training University, Tehran, Iran
Hossein Sirousi - Department of Electrical Engineering, Shahid Rajaee Teachers Training University, Tehran, Iran
Abstract:In many applications of noise cancellation the changes in signal characteristics could be quite fast. This requires the utilization of adaptive
algorithms, which converge rapidly. Least mean square (LMS) and Normalized LMS (NLMS) adaptive filters have been used in a wide range of signal processing applications because of its simplicity in computation and implementation. The Recursive Least Squares (RLS) algorithm has established itself as the “ultimate” adaptive filtering algorithm in the sense that it is the adaptive filter exhibiting the best convergence behavior. Unfortunately, practical implementations of this algorithm are often associated with high computational complexity and/or poor numerical
properties. In this paper we have performed and compared these classical adaptive filters for attenuating noise in speech signals. In each algorithm, the optimum order of filter of adaptive algorithms have also been found through experiments.
Keywords:Adaptive Filter, Least Mean Squares, Recursive Least Squares, Noise Cancellation
COI code: ISCEE09_036
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Esfand Abadi, Mohammad Shams & Hossein Sirousi, 2006, Application of Classical Adaptive Filters in Speech Enhancement, 09th Iranian Student Conference on Electrical Engineering, تهران, دانشگاه تهران, https://www.civilica.com/Paper-ISCEE09-ISCEE09_036.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Esfand Abadi, Mohammad Shams & Hossein Sirousi, 2006)
Second and more: (Esfand Abadi & Sirousi, 2006)
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Type: state university
Paper No.: 25985
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