  The attention-based Transformers have been increasingly applied to audio
classification because of their global receptive field and ability to handle
long-term dependency. However, the existing frameworks which are mainly
extended from the Vision Transformers are not perfectly compatible with audio
signals. In this paper, we introduce a Causal Audio Transformer (CAT)
consisting of a Multi-Resolution Multi-Feature (MRMF) feature extraction with
an acoustic attention block for more optimized audio modeling. In addition, we
propose a causal module that alleviates over-fitting, helps with knowledge
transfer, and improves interpretability. CAT obtains higher or comparable
state-of-the-art classification performance on ESC50, AudioSet and UrbanSound8K
datasets, and can be easily generalized to other Transformer-based models.
