Abstract
Adaptive efficient mechanism eliminates varying environmental noise embedded in speech signals, since the eigenvalue spread has a great influence on the convergence behavior of adaptive algorithms. The inefficient least mean square (LMS) algorithm for ill-conditioned signals, with high eigenvalue spread in the autocorrelation matrix, hence slow convergence and degraded signal quality are observed. Meanwhile, the Recursive Least Squares (RLS) solved this problem at the expense of high computational power. For these purposes, adaptive filtering offers a viable alternative to be used in various noise cancellation applications. In this paper, adaptive set-membership filtering based on a combination of a selective adaptive line enhancer with optimized set-membership filtering approach for single input noise cancellation system was proposed. The adaptive selection from a set of multiple adaptive algorithms to operate according to the characteristics of noise signals. The simulation results showed the capability of proposed algorithm to eliminate different types of environmental noise with fast convergence, reduction in computational complexity and improvement in signal-to-noise ratio when compared with an equivalent system using a single adaptive algorithm. The computational complexity of the proposed approach showed reduction of nearly 90% compared to the RLS and converged in about 6.25 msec.
| Original language | English |
|---|---|
| Title of host publication | 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 356-361 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509028894 |
| DOIs | |
| Publication status | Published - 27 Mar 2017 |
| Event | 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016 - Putrajaya, Malaysia Duration: 14 Nov 2016 → 16 Nov 2016 |
Other
| Other | 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016 |
|---|---|
| Country | Malaysia |
| City | Putrajaya |
| Period | 14/11/16 → 16/11/16 |
Fingerprint
Keywords
- Adaptive algorithm
- Adaptive filter
- Noise cancellation
- Speech enhancement
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Biomedical Engineering
- Control and Systems Engineering
- Hardware and Architecture
- Computer Networks and Communications
- Instrumentation
Cite this
Adaptive line enhancer with selectable algorithms based on noise eigenvalue spread. / Ramli, Roshahliza M.; Noor, Ali O.Abid; Abdul Samad, Salina.
2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 356-361 7888069.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Adaptive line enhancer with selectable algorithms based on noise eigenvalue spread
AU - Ramli, Roshahliza M.
AU - Noor, Ali O.Abid
AU - Abdul Samad, Salina
PY - 2017/3/27
Y1 - 2017/3/27
N2 - Adaptive efficient mechanism eliminates varying environmental noise embedded in speech signals, since the eigenvalue spread has a great influence on the convergence behavior of adaptive algorithms. The inefficient least mean square (LMS) algorithm for ill-conditioned signals, with high eigenvalue spread in the autocorrelation matrix, hence slow convergence and degraded signal quality are observed. Meanwhile, the Recursive Least Squares (RLS) solved this problem at the expense of high computational power. For these purposes, adaptive filtering offers a viable alternative to be used in various noise cancellation applications. In this paper, adaptive set-membership filtering based on a combination of a selective adaptive line enhancer with optimized set-membership filtering approach for single input noise cancellation system was proposed. The adaptive selection from a set of multiple adaptive algorithms to operate according to the characteristics of noise signals. The simulation results showed the capability of proposed algorithm to eliminate different types of environmental noise with fast convergence, reduction in computational complexity and improvement in signal-to-noise ratio when compared with an equivalent system using a single adaptive algorithm. The computational complexity of the proposed approach showed reduction of nearly 90% compared to the RLS and converged in about 6.25 msec.
AB - Adaptive efficient mechanism eliminates varying environmental noise embedded in speech signals, since the eigenvalue spread has a great influence on the convergence behavior of adaptive algorithms. The inefficient least mean square (LMS) algorithm for ill-conditioned signals, with high eigenvalue spread in the autocorrelation matrix, hence slow convergence and degraded signal quality are observed. Meanwhile, the Recursive Least Squares (RLS) solved this problem at the expense of high computational power. For these purposes, adaptive filtering offers a viable alternative to be used in various noise cancellation applications. In this paper, adaptive set-membership filtering based on a combination of a selective adaptive line enhancer with optimized set-membership filtering approach for single input noise cancellation system was proposed. The adaptive selection from a set of multiple adaptive algorithms to operate according to the characteristics of noise signals. The simulation results showed the capability of proposed algorithm to eliminate different types of environmental noise with fast convergence, reduction in computational complexity and improvement in signal-to-noise ratio when compared with an equivalent system using a single adaptive algorithm. The computational complexity of the proposed approach showed reduction of nearly 90% compared to the RLS and converged in about 6.25 msec.
KW - Adaptive algorithm
KW - Adaptive filter
KW - Noise cancellation
KW - Speech enhancement
UR - http://www.scopus.com/inward/record.url?scp=85018176263&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85018176263&partnerID=8YFLogxK
U2 - 10.1109/ICAEES.2016.7888069
DO - 10.1109/ICAEES.2016.7888069
M3 - Conference contribution
AN - SCOPUS:85018176263
SP - 356
EP - 361
BT - 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, ICAEES 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -