  In this paper, we address a sub-topic of the broad domain of audio
enhancement, namely musical audio bandwidth extension. We formulate the
bandwidth extension problem using deep neural networks, where a band-limited
signal is provided as input to the network, with the goal of reconstructing a
full-bandwidth output. Our main contribution centers on the impact of the
choice of low pass filter when training and subsequently testing the network.
For two different state of the art deep architectures, ResNet and U-Net, we
demonstrate that when the training and testing filters are matched,
improvements in signal-to-noise ratio (SNR) of up to 7dB can be obtained.
However, when these filters differ, the improvement falls considerably and
under some training conditions results in a lower SNR than the band-limited
input. To circumvent this apparent overfitting to filter shape, we propose a
data augmentation strategy which utilizes multiple low pass filters during
training and leads to improved generalization to unseen filtering conditions at
test time.
