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.

我们提出了一种无监督 - GFANC 方法，通过将协处理器和实时控制器集成到端到端可微的自适应噪声控制系统中，简化了一维卷积神经网络（1D CNN）的训练过程并提高了其实用性，以累积的二次误差信号作为训练 1D CNN 的损失函数，该无监督学习范式不仅省略了标记过程，而且在真实噪声实验中表现出比有监督 GFANC 方法更好的降噪性能。