Transformer structures have demonstrated outstanding skills in the deep learning space recently, significantly increasing the accuracy of models across a variety of domains. Researchers have started to question whether such a sophisticated network structure is actually necessary and whether equally outstanding results can be reached with reduced inference cost due to its complicated network topology and high inference cost. In order to prove the Mixer's efficacy on three datasets Speech Commands, UrbanSound8k, and CASIA Chinese Sentiment Corpus this paper applies amore condensed version of the Mixer to an audio classification task and conducts comparative experiments with the Transformer-based Audio Spectrogram Transformer (AST)model. In addition, this paper conducts comparative experiments on the application of several activation functions in Mixer, namely GeLU, Mish, Swish and Acon-C. Further-more, the use of various activation functions in Mixer, including GeLU, Mish, Swish, and Acon-C, is compared in this research through comparison experiments. Additionally, some AST model flaws are highlighted, and the model suggested in this study is improved as a result. In conclusion, a model called the Audio Spectrogram Mixer, which is the first model for audio classification with Mixer, is suggested in this study and the model's future directions for improvement are examined.

翻译：Transformer结构近年来在深度学习领域展现出卓越的能力，显著提升了各类模型的准确率。然而，由于其复杂的网络拓扑与高昂的推理成本，研究者开始质疑是否真的需要如此复杂的网络结构，以及能否在降低推理成本的同时取得同样出色的效果。为验证混合器在三个数据集（Speech Commands、UrbanSound8k及CASIA中文情感语料库）上的有效性，本文将一种更精简的混合器应用于音频分类任务，并与基于Transformer的音频频谱Transformer模型进行对比实验。此外，本文通过对比实验探究了混合器中多种激活函数（包括GeLU、Mish、Swish与Acon-C）的应用效果，并揭示了AST模型的部分缺陷，据此对本研究提出的模型进行了改进。最终，本文提出了音频频谱混合器模型——首个基于混合器的音频分类模型，并展望了该模型的未来优化方向。