The filtered-x recursive least square (FxRLS) algorithm is widely used in the active noise control system and has achieved great success in some complex de-noising environments, such as the cabin in vehicles and aircraft. However, its performance is sensitive to some user-defined parameters such as the forgetting factor and initial gain. Once these parameters are not selected properly, the de-noising effect of FxRLS will deteriorate. Moreover, the tracking performance of FxRLS for mutation is still restricted to a certain extent. To solve the above problems, this paper proposes a new proportional FxRLS (PFxRLS) algorithm. The forgetting factor and initial gain sensitivity are successfully reduced without introducing new turning parameters. The de-noising level and tracking performance have also been improved. Moreover, the momentum technique is introduced in PFxRLS to further improve its robustness and de-noising level. To ensure stability, its convergence condition is also discussed in this paper. The effectiveness of the proposed algorithms is illustrated by simulations and experiments with different user-defined parameters and time-varying noise environments.

Noise cancellation in a reverberation room is vital to hearing health. Although lots of methods in active noise control have been developed to solve this problem, as a matter of fact, because of the existence of irregular room distribution and related interference of adaptive filters, the noise reduction level and robustness need to be improved. In this work, a dropout leaky control strategy is proposed to improve the attenuation level and keep low less output of secondary loudspeakers in an irregular reverberation room. More exactly, the leaky filtered-x least mean square (LFxLMS) method is applied to decrease the related interference of adaptive filters. Then, each hidden leakage factor of LFxLMS is estimated by Kullback–Leibler (KL) divergence. Furthermore, importance factor is introduced to keep several different noise cancellation areas. The results of 2D dimensional simulations confirm the proposed algorithm has better Global noise reduction.

The equipment in the rehabilitation room can generate noise, affecting patients’ emotions and delaying their recovery. To create a quiet area in the rehabilitation room, a new adaptive sound zone strategy-based hybrid adaptive filter and deep learning network for a multi-channel active noise control (ANC) system is proposed. Firstly, to enhance the attenuation level in critical areas and achieve better robustness, a novel sound zone control strategy is integrated into the cost function of the proposed multi-channel ANC algorithm. Then, an improved hybrid filtered-x normalized least mean square (FxNLMS) and convolutional neural network-based long-short-term memory (CNN-LSTM) is utilized instead of a single adaptive filter to reduce time-varying and long-term periodic noise. In addition, a new phase-tracking filter is employed to assist the CNN-LSTM in enhancing its phase-tracking ability. Moreover, the proposed hybrid FxNLMS and CNN-LSTM multi-channel network is more effective at attenuating time-varying noise than the conventional single filter network. Meanwhile, it offers superior noise reduction and real-time tracking performance compared to the deep ANC network. And compared to the conventional delay compensation method, the proposed phase tracking filter demonstrates a smaller statistical average phase error. Furthermore, simulations using recorded noise in a rehabilitation room model illustrate the efficiency of the proposed method.

Acoustic imaging intuitively illustrates the locations of sound sources and has a wide range of applications, including fault detection. However, this technology requires a substantial number of microphones, which increases the complexity and cost of the data acquisition system. To reduce the number of microphones while maintaining the performance of acoustic imaging, this article introduces a concentric circular nested array (CCNA) design method. By extending the array using a differential coarray model, a virtual uniform concentric circular array is achieved. This virtual array preserves the advantages of the circular array structure while minimizing the number of required microphones. The performance of the proposed array is validated through simulation experiments. The simulation results demonstrate that the CCNA offers higher resolution, a greater dynamic range (DR), and improved positioning accuracy in acoustic imaging compared to other arrays. Finally, this article compares the acoustic imaging results of the CCNA with those of the uniform circular array (UCA) through experiments. The experimental results reveal that the positioning accuracy of the proposed CCNA surpasses that of the UCA, thereby confirming the superiority of the proposed design.

The air-conditioner noise in a rehabilitation room can seriously affect the mental state of patients. However, the existing single-layer active noise control (ANC) filters may fail to attenuate the complicated harmonic noise, and the deep recursive ANC method may fail to work in real time. To solve the problem, in a bending-pipe model, a new hybrid adaptive self-loading filtered-x least-mean-square (ASL-FxLMS) and convolutional neural network-gate recurrent unit (CNN-GRU) network is proposed. At first, based on the recursive GRU translation core, an improved CNN-GRU network with multi-head attention layers is proposed. Especially for complicated harmonic noises with more or fewer frequencies than harmonic models, the attenuation performance will be improved. In addition, its structure is optimized to decrease the computing load. In addition, an improved time-delay estimator is applied to improve the real-time ANC performance of CNN-GRU. Meanwhile, an adaptive self-loading FxLMS algorithm has been developed to deal with the uncertain components of complicated harmonic noise. Moreover, to achieve balance attenuation, robustness, and tracking performance, the ASL-FxLMS and CNN-GRU are connected by a convex combination structure. Furthermore, theoretical analysis and simulations are also conducted to show the effectiveness of the proposed method.