Asca: less audio data is more insightful
Xiang Li Junhao Chen Chao Li Hongwu Lv
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 https://github.com/LeeCiang/ASCA.