  Previous studies in automated audio captioning have faced difficulties in
accurately capturing the complete temporal details of acoustic scenes and
events within long audio sequences. This paper presents AudioLog, a large
language models (LLMs)-powered audio logging system with hybrid token-semantic
contrastive learning. Specifically, we propose to fine-tune the pre-trained
hierarchical token-semantic audio Transformer by incorporating contrastive
learning between hybrid acoustic representations. We then leverage LLMs to
generate audio logs that summarize textual descriptions of the acoustic
environment. Finally, we evaluate the AudioLog system on two datasets with both
scene and event annotations. Experiments show that the proposed system achieves
exceptional performance in acoustic scene classification and sound event
detection, surpassing existing methods in the field. Further analysis of the
prompts to LLMs demonstrates that AudioLog can effectively summarize long audio
sequences. To the best of our knowledge, this approach is the first attempt to
leverage LLMs for summarizing long audio sequences.
