## 1 Introduction  
Active noise control (ANC) is a technology used to reduce noise in the target area by actively generating a sound field with an external sound source and overlaying the existing noise sound field. ANC technology takes the noise signal as input and adjusts the controller, to generate a secondary noise signal, thereby achieving noise control. However, when the noise signal changes with time and the noise type can not be predicted in advance, it difficult to track the noise in real time. The noise type in a real environment is usually unknown; therefore, multiple groups of control filter banks should be designed in an ANC system to achieve the noise suppression effect with higher precision. This study paper attempted to solve these two problems using feature extraction and regression algorithm with ensemble learning. Variational mode decomposition (VMD), proposed by [PERSON] in 2014 [1], is an adaptive and completely non-recursive mode variation signal processing method. VMD decomposes the signal into several signals of different frequency scales. VMD can determine the number of modes decomposed from the signal based on the actual situation, adaptively match the best central frequency and finite bandwidth of each mode in the search and solution process and achieve the effective separation and frequency domain division of the intrinsic mode components in the signal. Recently, VMD has been widely used in noise feature extraction. [PERSON] combined VMD with k-nearest neighbor mutual information to extract features of ship-radiated noise [2]. [PERSON] proposed a secondary decomposition de-noising method for underwater acoustic signals with VMD [3]. [PERSON] applied VMD to obtain the noise intensity in intrinsic mode functions (IMFs) [4]. [PERSON] applied the mini-batch multivariate VMD to achieve the de-noising of random seismic noise [5]. [PERSON] and [PERSON] proposed a new method using VMD and a convolutional neural network to attenuate the white noise of a seismic trace [6]. [PERSON] conducted a comparative investigation of VMD and empirical mode decomposition for the noise reduction of transient electromagnetic signals. The research suggests that VMD is expected to be used for signal interference suppression in the field of transient electromagnetic detection in the future [7]. Ensemble learning is a growing topic of interest in the field of pattern recognition and machine learning. It has attracted great attention, as it can significantly improve the accuracy and generalizability of learning systems [8; 9]. In general, ensemble learning is divided into bagging, boosting and stacking and each of these three stages use algorithm designs based on the tree model. Advantages of a random forest are mainly reflected in two characteristics: It can process high-dimensional feature data without feature extraction [10; 11] (the importance of each feature would be evident after training) and it is conducive to fast speed parallel computation. Therefore, this paper reports on designs of an ANC system based on an ensemble learning algorithm of random forest. Bagging ensemble learning algorithms attain the optimal prediction model by training multiple classifiers in parallel and then taking the average value [12; 13]. The influence of the number of trees on prediction precision, however, should be taken into consideration when designing the bagging method. In theory, the effect should improve with an increase in the number of trees. The number of trees would not, however, affect the prediction precision greatly when it reaches a certain level. Stacking ensemble learning algorithms improve the prediction precision of the classifier by stacking individual learners together, but they are not suitable for online running due to their long computation time. In comparison with bagging and stacking ensemble learning algorithms, a boosting ensemble learning algorithm has a lower computation cost and higher prediction precision [14; 15].