基于无监督学习的端到端无延迟生成式固定滤波器有源噪声控制
Unsupervised learning based end-to-end delayless generative fixed-filter active noise control
AI总结：
针对现有生成式固定滤波器有源噪声控制框架需标注噪声数据训练网络的问题，提出无监督GFANC方法，通过构建端到端可微系统以累积平方误差为损失训练网络，省去标注流程且降噪性能更优。
摘要：
我们此前提出的生成式固定滤波器有源噪声控制（generative fixed-filter active noise control, GFANC）框架通过协处理器与实时控制器的高效协同，实现了无延迟噪声控制。然而，协处理器中的一维卷积神经网络（1D CNN）需要使用带标注的噪声数据集进行初始训练。标注噪声数据可能耗费大量资源，还可能引入某些偏差。本文提出一种无监督GFANC（unsupervised-GFANC）方法，以简化1D CNN的训练流程并提升其实用性。训练过程中，协处理器与实时控制器被整合为一个端到端可微的ANC系统，使得我们能够将累积平方误差信号作为损失来训练1D CNN。在这种无监督学习范式下，unsupervised-GFANC方法不仅省去了标注流程，在真实噪声实验中还展现出优于有监督GFANC方法的降噪性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
