  In some DNNs for audio source separation, the relevant model parameters are
independent of the sampling frequency of the audio used for training.
Considering the application of dialogue separation, this is shown for two DNN
architectures: a U-Net and a fully-convolutional model. The models are trained
with audio sampled at 8 kHz. The learned parameters are transferred to models
for processing audio at 48 kHz. The separated audio sources are compared with
the ones produced by the same model architectures trained with 48 kHz versions
of the same training data. A listening test and computational measures show
that there is no significant perceptual difference between the models trained
with 8 kHz or with 48 kHz. This transferability of the learned parameters
allows for a faster and computationally less costly training. It also enables
using training datasets available at a lower sampling frequency than the one
needed by the application at hand, or using data collections with multiple
sampling frequencies.
