  The high temporal resolution of audio and our perceptual sensitivity to small
irregularities in waveforms make synthesizing at high sampling rates a complex
and computationally intensive task, prohibiting real-time, controllable
synthesis within many approaches. In this work we aim to shed light on the
potential of Conditional Implicit Neural Representations (CINRs) as lightweight
backbones in generative frameworks for audio synthesis.
  Our experiments show that small Periodic Conditional INRs (PCINRs) learn
faster and generally produce quantitatively better audio reconstructions than
Transposed Convolutional Neural Networks with equal parameter counts. However,
their performance is very sensitive to activation scaling hyperparameters. When
learning to represent more uniform sets, PCINRs tend to introduce artificial
high-frequency components in reconstructions. We validate this noise can be
minimized by applying standard weight regularization during training or
decreasing the compositional depth of PCINRs, and suggest directions for future
research.
