Fundamenatals of active noise control for local cancellation of noise

Hirave, Padma. P.; Pathak, Bageshree

doi:10.1109/icectech.2011.5941896

Cited by 3 publications

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Filter coefficients are updated continuously to attain estimation, the faster they attain minimal values, the higher is the convergence rate. The lower range of variation results in higher stability [23][24][25][26][27][28][29][30]. Based on this criterion, Table 2 shows the values of the filter coefficients generated by the least mean square (LMS), normalized least mean square (NLMS), sign-error least mean square (SELMS), sign-sign least mean square (SSLMS) algorithms to cancel the noise signal.…”

Section: Resultsmentioning

confidence: 99%

Vehicle Cabin Noise Cancellation Model Using Pre-Filter for Improved Convergence Rate and Better Stability

Kapoor¹,

Pathak²,

Rai³

et al. 2023

Communications

View full text Add to dashboard Cite

Adaptive algorithms are used in updating the filter coefficients for active noise cancellation applications in reduction of vehicle cabin noise. The performance of the adaptive algorithms in low-frequency noise cancellation depends on how efficiently it alters the filter coefficient to minimize the difference between the approximated signal and the original one.Here is proposed an active noise cancellation model, using the low pass fixed coefficient filter before the adaptive filter, in order to improve the performance of the adaptive algorithm. Convergence rate, SNR and error vector magnitude are analysed for of adaptive algorithm in support of our research results.

show abstract

Section: Resultsmentioning

confidence: 99%

Vehicle Cabin Noise Cancellation Model Using Pre-Filter for Improved Convergence Rate and Better Stability

Kapoor¹,

Pathak²,

Rai³

et al. 2023

Communications

View full text Add to dashboard Cite

show abstract

“…Thus, reducing the system's input component Bt because the used system does not have any input ut. The adjusted equation is shown in (6).…”

Section: Predicting the Statementioning

confidence: 99%

“…LMS is based on the steepest descent method but does not account for secondary path effects, making it impossible to generate a precise anti-noise signal. The FxLMS algorithm is computationally simple and includes secondary path effects [6]. Several ANC algorithms with improved convergence properties have been proposed, including ANC systems in the , 1997).…”

Section: Introductionmentioning

confidence: 99%

Underwater Acoustic Noise Cancellation Scheme Combining Kalman Filter With Adaptive FxNLMS

Rahman

Sarker

2023

International Journal of Computational and Applied Mathematics &Amp; Computer Science

View full text Add to dashboard Cite

Underwater environments are more challenging than that of terrestrial. The performance of a controller or the augmented system as a whole depends on the real measured data, so noise on data readings can be fatal. To effectively and adaptively control lower and higher frequency noise, the Active Noise Cancellation (ANC) was developed. Designing a system and the parameter of modified FxLMS for reducing noise, and disturbance of sensor data is the primary focus of this paper. The required equation will be analyzed and discussed briefly. Moreover, the system will be simulated in MATLAB and then the filtered result will be analyzed. Based on the simulation results, the proposed model can filter out signals with noise, particularly when there is a significant variation in the data and no knowledge of the noise frequency that might affect sensor readings.

show abstract

Adaptive Filtering Application in Cancellation of Speech Signal Reverberations in Different Reverberant Surroundings

Kapoor,

Pathak

2024

J. Inst. Eng. India Ser. B

View full text Add to dashboard Cite

Fundamenatals of active noise control for local cancellation of noise

Cited by 3 publications

References 3 publications

Vehicle Cabin Noise Cancellation Model Using Pre-Filter for Improved Convergence Rate and Better Stability

Vehicle Cabin Noise Cancellation Model Using Pre-Filter for Improved Convergence Rate and Better Stability

Underwater Acoustic Noise Cancellation Scheme Combining Kalman Filter With Adaptive FxNLMS

Adaptive Filtering Application in Cancellation of Speech Signal Reverberations in Different Reverberant Surroundings

Contact Info

Product

Resources

About