  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.
