# Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm
## 1 Introduction  
The Active Noise Control (ANC) system has been growing rapidly in recent years because of the failure of the conventional noise mitigation systems in the diminution of the error noise over the applications. The level of the residual noise is controlled by Least Mean Square (LMS) adaptive filters [1]. Narrowband and wideband noise sources are destructed by an ANC system based on the principle of destructive interference between the primary and secondary noise sources. Basic applications of the acoustic noise sources are fans, blowers, transformers, and engines. The ANC scheme diminishes the low-frequency noise, and the magnitude in response path can be determined by the performance of the ANC system [2]. The adaptation algorithms are used for the active noise and vibration control [3]. The multiple harmonics are reduced by the extension of noise sources. The signals are sampled at the beginning, and then, processed by a digital signal processing system; in which anti-noise is generated by the loudspeakers [4]. The FxLMS algorithm is used to compute the acoustic delay and transducer transfer function. As per the radiation impedance, the global sound control is achieved [5]. Any ANC system does not reduce the original amplitude of sounds because of the presence of low noise in the system. Due to the involvement of digital signal processing, the performance of ANC can be improved [6; 7]. The filtered error structure is exhibited to reduce the non-linear ANC with the linear secondary path. The statistical properties of the system provide robustness and reduce computational complexity [8]. The fast affine projection algorithm creates a trade-off between the speed and complexity of the method. The reduction of error can create the minimization of filter coefficients. The adaptive filters are used to cancel the noise present in the system. The acoustic plants are derived from removing thepoles, and Recursive Least Mean Square (RLMS) algorithm has been adapted as an attenuation system. It converges with random noise signal and narrowband input signals [9; 10]. To control the generator noise of real-time experiments, Adaptive Feedback Active Noise Control (AFANC) algorithm is used. The primary noise will be cancelled out according to the superposition principle. Thus, the anti-noise is generated with equal amplitude and phase to remove the original noise [11]. The harmonics and the noise are reduced to the level of blade passing frequency, at different flow rates. Turbulent and rotational blade passing noise is present in the system of the fan. The recursive least square algorithm makes the ANC scheme effective [12]. The controller coefficients are varied by the feedforward and feedback control. Due to the control of blade passing noise, the centrifugal noise is reduced. Undesirable noise is calibrated at the output of the system [13]. To digital ANC system, the analog feedback loop is added to reduce fluctuation in the system. The narrowband and wideband noise can be reduced by a hybrid headset system, and the stability of the FxLMS algorithm can be improved [14]. Any active noise can be controlled by modeling the secondary path online and offline, with faster convergence and high accuracy. Low residue noise and zero mean error can be achieved effectively [15]. The transfer function of the secondary path can be estimated between the secondary source and the error microphone; in which a primary noise exhibits non-linear distortion at the cancellation point. The secondary source can be created by using the FxLMS algorithm [16]. Noise cancellation occurs due to the reintroduction of noise at the position of the field. Primary noise propagates in the duct with high sound pressure. The non-minimum phase response is exhibited between the error phone and speaker [17]. The filter generates the zero error. Thus, the error signal appears with no delay. The gain value can control the speed of the adaptive filter [18].