Unsupervised learning based end-to-end delayless generative fixed-filter active noise control
<|reference_start|>Unsupervised learning based end-to-end delayless generative fixed-filter active noise control: 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 co-processor and real-time controller are integrated into an end-to-end differentiable ANC system. This enables us to use the accumulated squared error signal as the loss for training the 1D CNN. With this unsupervised learning paradigm, the unsupervised-GFANC method not only omits the labelling process but also exhibits better noise reduction performance compared to the supervised GFANC method in real noise experiments.<|reference_end|>
@article{luo2024unsupervised,
    title={Unsupervised learning based end-to-end delayless generative fixed-filter
  active noise control},
    author={Zhengding Luo, Dongyuan Shi, Xiaoyi Shen, Woon-Seng Gan},
    journal={arXiv preprint arXiv:2402.09460},
    year={2024},
    doi={10.1109/ICASSP48485.2024.10448277},
    archivePrefix={arXiv},
    eprint={2402.09460},
    primaryClass={eess.SP cs.LG}
}