Genetic Algorithm Applied to the Eigenvalue Equalization Filtered‐x LMS Algorithm (EE‐FXLMS)

Lovstedt, Stephan P.; Thomas, Jared J.; Sommerfeldt, Scott D.; Blotter, Jonathan D.

doi:10.1155/2008/791050

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…While these approaches work, they either increase the complexity of the control algorithm or are only effective for specific applications. Recently, a new approach called the eigenvalue equalization filtered-x least mean square (EE-FXLMS) algorithm has been developed by Sommerfeldt et al [23][24][25][26]. The technique is designed to overcome the frequency-dependent performance problem.…”

Section: Introductionmentioning

confidence: 99%

Virtual Secondary Path Algorithm for Multichannel Active Control of Vehicle Powertrain Noise

Duan¹,

Li²,

Lim

et al. 2013

Journal of Vibration and Acoustics

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An enhanced multiple-input multiple-output (MIMO) filtered-x least mean square (FXLMS) algorithm using improved virtual secondary path is proposed as the basis for an active noise control (ANC) system for treating vehicle powertrain noise. This new algorithm is developed to overcome the limitation caused by the frequency-dependent property of the standard FXLMS algorithm and to reduce the variation of convergence speed inherent in multiple-channel cases, in order to improve the overall performance of the control system. In this study, the convergence property of the proposed algorithm is analyzed in the frequency domain in order to yield a better understanding of the physical meaning of the virtual secondary path. In practice, because of the arrangement and sensitivities of the actuators (speakers), transducers (microphones), and physical environment, the magnitude response of the main secondary paths can be very different from each other. This difference will cause difficulty in the overall convergence of the algorithm, which will result in minimal attenuation at some of the channels. The proposed channel equalized (CE) virtual secondary path algorithm is designed to tackle this difficulty by equalizing the mean magnitude level of the main secondary paths and by adjusting other secondary paths correspondingly to keep the coupling effects among the control channels unchanged. The performance of the proposed algorithm is validated by analyzing a two-input two-output active powertrain noise control system.

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Section: Introductionmentioning

confidence: 99%

Virtual Secondary Path Algorithm for Multichannel Active Control of Vehicle Powertrain Noise

Duan¹,

Li²,

Lim

et al. 2013

Journal of Vibration and Acoustics

View full text Add to dashboard Cite

show abstract

“…This technique will overcome the speech signal's noisy conditions and extract the best speech parameters for the speech recognition system. One of the techniques that is usually used for best parameters selection and optimization is the genetic algorithm (GA).The GA procedure has been adopted in many speech processing systems, such as a tool for estimating the sinusoid signal [8] and noise control [9] as well as for improving the performance of the speech parameterizing algorithms [10][11][12][13] especially when the signal is embedded within the unwanted noise. This paper presents a technique that used a selective method ( genetic algorithm in this work) to serve as a selection procedure to produce the fitter channels' values according to the words being processed by the system.…”

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confidence: 99%

Speech recognition system based multi-level selective filter

Hassan

Ajel

Zubaidy

2022

J. Phys.: Conf. Ser.

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In this paper, an updated source-filter based method is used for speech recognition of English speakers. The key goal of this paper is to discuss the influence of using dynamic and selective Filterbank on the efficiency of the system in terms of identifying the word being said. Dynamic structures of the bandpass filter bank are clearly able to adjust their principles in conjunction with the speech database used. In this paper, the proposed methods adopted the idea of re-select or adjusting the filter parameters values at each iteration, in accordance with the word being processed. In other words, the values of the filter components (center frequencies and bandwidths) will adjust until it gets the best set of the filter components. The genetic algorithm (GA) will be used to maintain the components of the filter and each new population of the GA represents a new set of channels for the filter bank. Results show that the efficiency of the recognition system would be improved with each new set of filter components generated by the genetic algorithm. Speech recognition, Source-filter, Filterbank, genetic algorithm, adjusted filter, Dynamic system

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Genetic Algorithm Applied to the Eigenvalue Equalization Filtered‐x LMS Algorithm (EE‐FXLMS)

Cited by 2 publications

References 12 publications

Virtual Secondary Path Algorithm for Multichannel Active Control of Vehicle Powertrain Noise

Virtual Secondary Path Algorithm for Multichannel Active Control of Vehicle Powertrain Noise

Speech recognition system based multi-level selective filter

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