  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.
