  Neuron-level interpretations aim to explain network behaviors and properties
by investigating neurons responsive to specific perceptual or structural input
patterns. Although there is emerging work in the vision and language domains,
none is explored for acoustic models. To bridge the gap, we introduce
$\textit{AND}$, the first $\textbf{A}$udio $\textbf{N}$etwork
$\textbf{D}$issection framework that automatically establishes natural language
explanations of acoustic neurons based on highly-responsive audio.
$\textit{AND}$ features the use of LLMs to summarize mutual acoustic features
and identities among audio. Extensive experiments are conducted to verify
$\textit{AND}$'s precise and informative descriptions. In addition, we
demonstrate a potential use of $\textit{AND}$ for audio machine unlearning by
conducting concept-specific pruning based on the generated descriptions.
Finally, we highlight two acoustic model behaviors with analysis by
$\textit{AND}$: (i) models discriminate audio with a combination of basic
acoustic features rather than high-level abstract concepts; (ii) training
strategies affect model behaviors and neuron interpretability -- supervised
training guides neurons to gradually narrow their attention, while
self-supervised learning encourages neurons to be polysemantic for exploring
high-level features.
