@conference {35099,
	title = {A HYBRID DEEP-ONLINE LEARNING BASED METHOD FOR ACTIVE NOISE CONTROLIN WAVE DOMAIN},
	booktitle = {International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
	year = {2024},
	month = {April 14-19},
	address = {COEX, Seoul, Korea},
	abstract = {The traditional feedback Active Noise Control (ANC) algorithms arebuilt upon linear filters, which leads to reduced performance whendealing with real-world noise. Deep learning-based feedback ANCalgorithms have been proposed to overcome this problem. However,methods relying on pre-trained neural networks exhibit performancedegradation when encountering noise from unseen scenes inthe training dataset. This paper proposed a hybrid deep-online learningbased spatial ANC system which combines online learning withpre-trained deep neural networks. The proposed method can keepthe performance on noise from the trained scenes while improve theperformance of cancelling noise from new scenes. Additionally, byincorporating wave domain decomposition, this paper achieves noisecancellation over a control spatial region. Simulation experimentsvalidate the effectiveness of the combination of online learning anddeep learning in handling previously unseen noise. Furthermore, theefficiency of wave domain decomposition in spatial noise cancellationis also verified.},
	author = {Donghang Wu and Xihong Wu and Tianshu Qu}
}
