Modified LMS algorithms for speech processing with an adaptive noise canceller
A desired signal corrupted by additive noise can often be recovered by an adaptive noise canceller using the least mean squares (LMS) algorithm. A major disadvantage of the LMS algorithm is its excess mean-squared error, or misadjustment, which increases linearly with the desired signal power, This...
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| Published in | IEEE transactions on speech and audio processing Vol. 6; no. 4; pp. 338 - 351 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
New York, NY
IEEE
01.07.1998
Institute of Electrical and Electronics Engineers |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1063-6676 |
| DOI | 10.1109/89.701363 |
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| Abstract | A desired signal corrupted by additive noise can often be recovered by an adaptive noise canceller using the least mean squares (LMS) algorithm. A major disadvantage of the LMS algorithm is its excess mean-squared error, or misadjustment, which increases linearly with the desired signal power, This leads to degrading performance when the desired signal exhibits large power fluctuations and is a serious problem in many speech processing applications. This work considers two modified LMS algorithms, the weighted sum and sum methods, designed to solve this problem by reducing the size of the steps in the weight update equation when the desired signal is strong. The weighted sum method is derived from an optimal method (also developed in this work), which is not generally applicable because it requires quantities unavailable in a practical system. The previously proposed, but ad hoc, sum method is analyzed and compared to the weighted sum method. Analysis of the two modified LMS algorithms indicates that either one provides substantial improvements in the presence of strong desired signals and similar performance in the presence of weak desired signals, relative to the unmodified LMS algorithm. Computer simulations with both uncorrelated Gaussian noise and speech signals confirm the results of the analysis and demonstrate the effectiveness of the modified algorithms. The modified LMS algorithms are particularly suited for signals (such as speech) that exhibit large fluctuations in short-time power levels. |
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| AbstractList | A desired signal corrupted by additive noise can often be recovered by an adaptive noise canceller using the least mean squares (LMS) algorithm. A major disadvantage of the LMS algorithm is its excess mean-squared error, or misadjustment, which increases linearly with the desired signal power, This leads to degrading performance when the desired signal exhibits large power fluctuations and is a serious problem in many speech processing applications. This work considers two modified LMS algorithms, the weighted sum and sum methods, designed to solve this problem by reducing the size of the steps in the weight update equation when the desired signal is strong. The weighted sum method is derived from an optimal method (also developed in this work), which is not generally applicable because it requires quantities unavailable in a practical system. The previously proposed, but ad hoc, sum method is analyzed and compared to the weighted sum method. Analysis of the two modified LMS algorithms indicates that either one provides substantial improvements in the presence of strong desired signals and similar performance in the presence of weak desired signals, relative to the unmodified LMS algorithm. Computer simulations with both uncorrelated Gaussian noise and speech signals confirm the results of the analysis and demonstrate the effectiveness of the modified algorithms. The modified LMS algorithms are particularly suited for signals (such as speech) that exhibit large fluctuations in short-time power levels A desired signal corrupted by additive noise can often be recovered by an adaptive noise canceller using the least mean squares (LMS) algorithm. A major disadvantage of the LMS algorithm is its excess mean-squared error, or misadjustment, which increases linearly with the desired signal power, This leads to degrading performance when the desired signal exhibits large power fluctuations and is a serious problem in many speech processing applications. This work considers two modified LMS algorithms, the weighted sum and sum methods, designed to solve this problem by reducing the size of the steps in the weight update equation when the desired signal is strong. The weighted sum method is derived from an optimal method (also developed in this work), which is not generally applicable because it requires quantities unavailable in a practical system. The previously proposed, but ad hoc, sum method is analyzed and compared to the weighted sum method. Analysis of the two modified LMS algorithms indicates that either one provides substantial improvements in the presence of strong desired signals and similar performance in the presence of weak desired signals, relative to the unmodified LMS algorithm. Computer simulations with both uncorrelated Gaussian noise and speech signals confirm the results of the analysis and demonstrate the effectiveness of the modified algorithms. The modified LMS algorithms are particularly suited for signals (such as speech) that exhibit large fluctuations in short-time power levels. |
| Author | Greenberg, J.E. |
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| Cites_doi | 10.1007/BF01012112 10.1109/TCS.1986.1085982 10.1109/78.236504 10.1016/0167-6393(90)90019-6 10.1080/00016489.1990.12088412 10.1109/31.31337 10.1109/TASSP.1986.1164798 10.1109/ICASSP.1983.1172047 10.1109/31.1709 10.1109/TASSP.1987.1165232 10.1109/TCOM.1980.1094711 10.1016/0165-1684(90)90013-O 10.1109/89.397095 10.1109/PROC.1980.11774 10.1109/TAC.1967.1098599 10.1109/78.193228 10.1109/78.139261 10.1109/TSP.1993.193134 10.1109/89.221372 10.1109/JSAC.1984.1146062 10.1109/TASSP.1986.1164777 10.1109/TASSP.1981.1163596 10.1109/TASSP.1986.1164814 10.1109/TASSP.1982.1163933 10.1109/TASSP.1987.1165167 10.1109/TAP.1982.1142739 10.1121/1.402446 10.1121/1.381436 10.1121/1.406676 10.1109/78.286951 10.1109/78.286952 10.1109/TASSP.1986.1164914 10.1109/78.218137 10.1109/ICASSP.1980.1170939 |
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| Keywords | Additive noise Gaussian noise Correlation analysis Least squares method Noise reduction Adaptive filtering Computational complexity Speech processing Implementation |
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| SubjectTerms | Additive noise Algorithm design and analysis Applied sciences Degradation Exact sciences and technology Fluctuations Information, signal and communications theory Least squares approximation Noise cancellation Signal analysis Signal processing Speech analysis Speech processing Telecommunications and information theory |
| Title | Modified LMS algorithms for speech processing with an adaptive noise canceller |
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