  Zero-shot audio classification aims to recognize and classify a sound class
that the model has never seen during training. This paper presents a novel
approach for zero-shot audio classification using automatically generated sound
attribute descriptions. We propose a list of sound attributes and leverage
large language model's domain knowledge to generate detailed attribute
descriptions for each class. In contrast to previous works that primarily
relied on class labels or simple descriptions, our method focuses on
multi-dimensional innate auditory attributes, capturing different
characteristics of sound classes. Additionally, we incorporate a contrastive
learning approach to enhance zero-shot learning from textual labels. We
validate the effectiveness of our method on VGGSound and AudioSet\footnote{The
code is available at \url{https://www.github.com/wsntxxn/AttrEnhZsAc}.}. Our
results demonstrate a substantial improvement in zero-shot classification
accuracy. Ablation results show robust performance enhancement, regardless of
the model architecture.
