  Transformers have rapidly become the preferred choice for audio
classification, surpassing methods based on CNNs. However, Audio Spectrogram
Transformers (ASTs) exhibit quadratic scaling due to self-attention. The
removal of this quadratic self-attention cost presents an appealing direction.
Recently, state space models (SSMs), such as Mamba, have demonstrated potential
in language and vision tasks in this regard. In this study, we explore whether
reliance on self-attention is necessary for audio classification tasks. By
introducing Audio Mamba (AuM), the first self-attention-free, purely SSM-based
model for audio classification, we aim to address this question. We evaluate
AuM on various audio datasets - comprising six different benchmarks - where it
achieves comparable or better performance compared to well-established AST
model.
