Audio recognition in specialized areas such as birdsong and submarine
acoustics faces challenges in large-scale pre-training due to the limitations
in available samples imposed by sampling environments and specificity
requirements. While the Transformer model excels in audio recognition, its
dependence on vast amounts of data becomes restrictive in resource-limited
settings. Addressing this, we introduce the Audio Spectrogram Convolution
Attention (ASCA) based on CoAtNet, integrating a Transformer-convolution hybrid
architecture, novel network design, and attention techniques, further augmented
with data enhancement and regularization strategies. On the BirdCLEF2023 and
AudioSet(Balanced), ASCA achieved accuracies of 81.2% and 35.1%, respectively,
significantly outperforming competing methods. The unique structure of our
model enriches output, enabling generalization across various audio detection
tasks. Our code can be found at this https URL

基于 CoAtNet 的 ASCA 结合了 Transformer-convolution 混合架构、新颖的网络设计和注意力技术，结合数据增强和正则化策略，在 BirdCLEF2023 和 AudioSet (Balanced) 测试中分别获得了 81.2% 和 35.1% 的准确率，明显优于竞争方法。