  Large language models reveal deep comprehension and fluent generation in the
field of multi-modality. Although significant advancements have been achieved
in audio multi-modality, existing methods are rarely leverage language model
for sound event detection (SED). In this work, we propose an end-to-end
framework for understanding audio features while simultaneously generating
sound event and their temporal location. Specifically, we employ pretrained
acoustic models to capture discriminative features across different categories
and language models for autoregressive text generation. Conventional methods
generally struggle to obtain features in pure audio domain for classification.
In contrast, our framework utilizes the language model to flexibly understand
abundant semantic context aligned with the acoustic representation. The
experimental results showcase the effectiveness of proposed method in enhancing
timestamps precision and event classification.
