  Most audio processing pipelines involve transformations that act on
fixed-dimensional input representations of audio. For example, when using the
Short Time Fourier Transform (STFT) the DFT size specifies a fixed dimension
for the input representation. As a consequence, most audio machine learning
models are designed to process fixed-size vector inputs which often prohibits
the repurposing of learned models on audio with different sampling rates or
alternative representations. We note, however, that the intrinsic spectral
information in the audio signal is invariant to the choice of the input
representation or the sampling rate. Motivated by this, we introduce a novel
way of processing audio signals by treating them as a collection of points in
feature space, and we use point cloud machine learning models that give us
invariance to the choice of representation parameters, such as DFT size or the
sampling rate. Additionally, we observe that these methods result in smaller
models, and allow us to significantly subsample the input representation with
minimal effects to a trained model performance.
