  Recently, Transformers have been introduced into the field of acoustics
recognition. They are pre-trained on large-scale datasets using methods such as
supervised learning and semi-supervised learning, demonstrating robust
generality--It fine-tunes easily to downstream tasks and shows more robust
performance. However, the predominant fine-tuning method currently used is
still full fine-tuning, which involves updating all parameters during training.
This not only incurs significant memory usage and time costs but also
compromises the model's generality. Other fine-tuning methods either struggle
to address this issue or fail to achieve matching performance. Therefore, we
conducted a comprehensive analysis of existing fine-tuning methods and proposed
an efficient fine-tuning approach based on Adapter tuning, namely AAT. The core
idea is to freeze the audio Transformer model and insert extra learnable
Adapters, efficiently acquiring downstream task knowledge without compromising
the model's original generality. Extensive experiments have shown that our
method achieves performance comparable to or even superior to full fine-tuning
while optimizing only 7.118% of the parameters. It also demonstrates
superiority over other fine-tuning methods.
