  Since convolutional neural networks (CNNs) have revolutionized the image
processing field, they have been widely applied in the audio context. A common
approach is to convert the one-dimensional audio signal time series to
two-dimensional images using a time-frequency decomposition method. Also it is
common to discard the phase information. In this paper, we propose to map
one-dimensional audio waveforms to two-dimensional images using space filling
curves (SFCs). These mappings do not compress the input signal, while
preserving its local structure. Moreover, the mappings benefit from progress
made in deep learning and the large collection of existing computer vision
networks. We test eight SFCs on two keyword spotting problems. We show that the
Z curve yields the best results due to its shift equivariance under convolution
operations. Additionally, the Z curve produces comparable results to the widely
used mel frequency cepstral coefficients across multiple CNNs.
