Infrasound event classification with multi-channel multi-scale convolutional attention network

Infrasound signals are prone to atmospheric multipath effects during propagation, resulting in signal aliasing and interference that adversely affect classification performance. To overcome this challenge, a method for classifying infrasound signals based on multi-channel multi-scale convolutional attention network is proposed. The proposed approach first employs the complementary ensemble empirical mode decomposition with adaptive noise to decompose the original signal, then performs dimensional concatenation of four intrinsic mode functions along the feature axis to construct a multi-channel feature representation, effectively mitigating aliasing between multipath components and direct signals. Subsequently, a multi-scale convolutional attention network is constructed, utilizing multi-scale kernels to simultaneously capture both long-term dependencies and local transient features, with a channel-spatial attention mechanism adaptively emphasizing discriminative information. Experimental results demonstrate the method’s superior performance in multipath environments, achieving 82.76% average classification accuracy on the infrasound dataset and outperforming two conventional classification approaches, thereby confirming its effectiveness and robustness for infrasound signal classification.