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The high temporal resolution of audio and our perceptual sensitivity to smallirregularities in waveforms make synthesizing at high sampling rates a complexand computationally intensive task, prohibiting real-time, controllablesynthesis within many approaches. In this work we aim to shed light on thepotential of Conditional Implicit Neural Representations (CINRs) as lightweightbackbones in generative frameworks for audio synthesis.  Our experiments show that small Periodic Conditional INRs (PCINRs) learnfaster and generally produce quantitatively better audio reconstructions thanTransposed Convolutional Neural Networks with equal parameter counts. However,their performance is very sensitive to activation scaling hyperparameters. Whenlearning to represent more uniform sets, PCINRs tend to introduce artificialhigh-frequency components in reconstructions. We validate this noise can beminimized by applying standard weight regularization during training ordecreasing the compositional depth of PCINRs, and suggest directions for futureresearch.

Towards Lightweight Controllable Audio Synthesis with Conditional Implicit Neural Representations