  Recently, MLP structures have regained popularity, with MLP-Mixer standing
out as a prominent example. In the field of computer vision, MLP-Mixer is noted
for its ability to extract data information from both channel and token
perspectives, effectively acting as a fusion of channel and token information.
Indeed, Mixer represents a paradigm for information extraction that amalgamates
channel and token information. The essence of Mixer lies in its ability to
blend information from diverse perspectives, epitomizing the true concept of
"mixing" in the realm of neural network architectures. Beyond channel and token
considerations, it is possible to create more tailored mixers from various
perspectives to better suit specific task requirements. This study focuses on
the domain of audio recognition, introducing a novel model named Audio
Spectrogram Mixer with Roll-Time and Hermit FFT (ASM-RH) that incorporates
insights from both time and frequency domains. Experimental results demonstrate
that ASM-RH is particularly well-suited for audio data and yields promising
outcomes across multiple classification tasks. The models and optimal weights
files will be published.
