Unsupervised learning based end-to-end delayless generative fixed-filter active noise control

arXiv:2402.09460v1 Announce Type: cross
Abstract: Delayless noise control is achieved by our earlier generative fixed-filter active noise control (GFANC) framework through efficient coordination between the co-processor and real-time controller. However, the one-dimensional convolutional neural network (1D CNN) in the co-processor requires initial training using labelled noise datasets. Labelling noise data can be resource-intensive and may introduce some biases. In this paper, we propose an unsupervised-GFANC approach to simplify the 1D CNN training process and enhance its practicality. During training, the …

abstract arxiv cnn control convolutional neural network cs.lg data datasets eess.sp filter framework generative labelling network neural network noise processor real-time through training type unsupervised unsupervised learning