Algorithm 1 AdaBN for WDCNN
Input:	Input of neuron i in BN layers of WDCNN for unlabeled target signal p, $x_t^{\left(i\right)}\left(p\right)\in x_t^{\left(i\right)}$,where $x_t^{\left(i\right)}=\{x_t^{\left(i\right)}\left(1\right),\dots ,x_t^{\left(i\right)}\left(n\right)\}$ The trained scale and shift parameters ${\gamma }_s^{\left(i\right)}$ and ${\beta }_s^{\left(i\right)}$ for neuron i using the labeled source signals.
output:	Adjusted structure of WDCNN
For	Each neuron i and each signal p in target domain Calculate the mean and variance of all the samples in target domain: ${\mu }_t^{\left(i\right)}\leftarrow E[x_t^{\left(i\right)}]$ ${\sigma }_t^{\left(i\right)}\leftarrow Var[x_t^{\left(i\right)}]$ Calculate the BN output by: ${\hat{x}}_t^{\left(i\right)}\left(p\right)=\frac{x_t^{\left(i\right)}\left(p\right)-{\mu }_t^{\left(i\right)}}{{\sigma }_t^{\left(i\right)}}$ ${\hat{y}}_t^{\left(i\right)}\left(p\right)={\gamma }^{\left(i\right)}{\hat{x}}_t^{\left(i\right)}\left(p\right){\beta }^{\left(i\right)}$
End for