  Deep neural networks are a promising tool for Audio Event Classification. In
contrast to other data like natural images, there are many sensible and
non-obvious representations for audio data, which could serve as input to these
models. Due to their black-box nature, the effect of different input
representations has so far mostly been investigated by measuring classification
performance. In this work, we leverage eXplainable AI (XAI), to understand the
underlying classification strategies of models trained on different input
representations. Specifically, we compare two model architectures with regard
to relevant input features used for Audio Event Detection: one directly
processes the signal as the raw waveform, and the other takes in its
time-frequency spectrogram representation. We show how relevance heatmaps
obtained via "Siren"{Layer-wise Relevance Propagation} uncover
representation-dependent decision strategies. With these insights, we can make
a well-informed decision about the best input representation in terms of
robustness and representativity and confirm that the model's classification
strategies align with human requirements.
