The majority of the recent works that address the interpretability of raw waveform based deep neural networks (DNNs) for audio processing focus on interpreting spectral and frequency response information, often limiting to visual and signal theoretic means of interpretation, solely for the first layer. This work proposes sonification, a method to interpret intermediate feature representations of sound event recognition (SER) 1D-convolutional neural networks (1D-CNNs) trained on raw waveforms by mapping these representations back into the discrete-time input signal domain, highlighting substructures in the input that maximally activate a feature map as intelligible acoustic events. Sonification is used to compare supervised and contrastive self-supervised feature representations, observing how the latter learn more acoustically discernible representations, especially in the deeper layers. A metric to quantify acoustic similarity between the interpretations and their corresponding inputs is proposed, and a layer-by-layer analysis of the trained feature representations using this metric supports the observations made
