A low computational complexity normalized subband adaptive filter algorithm employing signed regressor of input signal
Abstract ᅟ In this paper, the signed regressor normalized subband adaptive filter (SR-NSAF) algorithm is proposed. This algorithm is optimized by L 1-norm minimization criteria. The SR-NSAF has a fast convergence speed and a low steady-state error similar to the conventional NSAF. In addition, the p...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2018-04-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13634-018-0542-z |
| _version_ | 1819102294228074496 |
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| author | Mohammad Shams Esfand Abadi Mohammad Saeed Shafiee Mehrdad Zalaghi |
| author_facet | Mohammad Shams Esfand Abadi Mohammad Saeed Shafiee Mehrdad Zalaghi |
| author_sort | Mohammad Shams Esfand Abadi |
| collection | DOAJ |
| description | Abstract ᅟ In this paper, the signed regressor normalized subband adaptive filter (SR-NSAF) algorithm is proposed. This algorithm is optimized by L 1-norm minimization criteria. The SR-NSAF has a fast convergence speed and a low steady-state error similar to the conventional NSAF. In addition, the proposed algorithm has lower computational complexity than NSAF due to the signed regressor of the input signal at each subband. The theoretical mean-square performance analysis of the proposed algorithm in the stationary and nonstationary environments is studied based on the energy conservation relation and the steady-state, the transient, and the stability bounds of the SR-NSAF are predicated by the closed form expressions. The good performance of SR-NSAF is demonstrated through several simulation results in system identification, acoustic echo cancelation (AEC) and line EC (LEC) applications. The theoretical relations are also verified by presenting various experimental results. |
| first_indexed | 2024-12-22T01:32:16Z |
| format | Article |
| id | doaj.art-0cf378a2860446b991ef2ca6fd6a74ff |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-12-22T01:32:16Z |
| publishDate | 2018-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-0cf378a2860446b991ef2ca6fd6a74ff2022-12-21T18:43:28ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802018-04-012018112310.1186/s13634-018-0542-zA low computational complexity normalized subband adaptive filter algorithm employing signed regressor of input signalMohammad Shams Esfand Abadi0Mohammad Saeed Shafiee1Mehrdad Zalaghi2Faculty of Electrical Engineering, Shahid Rajaee Teacher Training UniversityFaculty of Electrical Engineering, Shahid Rajaee Teacher Training UniversityFaculty of Electrical Engineering, Shahid Rajaee Teacher Training UniversityAbstract ᅟ In this paper, the signed regressor normalized subband adaptive filter (SR-NSAF) algorithm is proposed. This algorithm is optimized by L 1-norm minimization criteria. The SR-NSAF has a fast convergence speed and a low steady-state error similar to the conventional NSAF. In addition, the proposed algorithm has lower computational complexity than NSAF due to the signed regressor of the input signal at each subband. The theoretical mean-square performance analysis of the proposed algorithm in the stationary and nonstationary environments is studied based on the energy conservation relation and the steady-state, the transient, and the stability bounds of the SR-NSAF are predicated by the closed form expressions. The good performance of SR-NSAF is demonstrated through several simulation results in system identification, acoustic echo cancelation (AEC) and line EC (LEC) applications. The theoretical relations are also verified by presenting various experimental results.http://link.springer.com/article/10.1186/s13634-018-0542-zNormalized subband adaptive filter (NSAF)Mean-square performanceSigned regressor (SR)L 1-norm |
| spellingShingle | Mohammad Shams Esfand Abadi Mohammad Saeed Shafiee Mehrdad Zalaghi A low computational complexity normalized subband adaptive filter algorithm employing signed regressor of input signal EURASIP Journal on Advances in Signal Processing Normalized subband adaptive filter (NSAF) Mean-square performance Signed regressor (SR)L 1-norm |
| title | A low computational complexity normalized subband adaptive filter algorithm employing signed regressor of input signal |
| title_full | A low computational complexity normalized subband adaptive filter algorithm employing signed regressor of input signal |
| title_fullStr | A low computational complexity normalized subband adaptive filter algorithm employing signed regressor of input signal |
| title_full_unstemmed | A low computational complexity normalized subband adaptive filter algorithm employing signed regressor of input signal |
| title_short | A low computational complexity normalized subband adaptive filter algorithm employing signed regressor of input signal |
| title_sort | low computational complexity normalized subband adaptive filter algorithm employing signed regressor of input signal |
| topic | Normalized subband adaptive filter (NSAF) Mean-square performance Signed regressor (SR)L 1-norm |
| url | http://link.springer.com/article/10.1186/s13634-018-0542-z |
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