Dilated convolution with learnable spacings (DCLS) is a recent convolution
method in which the positions of the kernel elements are learned throughout
training by backpropagation. Its interest has recently been demonstrated in
computer vision (ImageNet classification and downstream tasks). Here we show
that DCLS is also useful for audio tagging using the AudioSet classification
benchmark. We took two state-of-the-art convolutional architectures using
depthwise separable convolutions (DSC), ConvNeXt and ConvFormer, and a hybrid
one using attention in addition, FastViT, and drop-in replaced all the DSC
layers by DCLS ones. This significantly improved the mean average precision
(mAP) with the three architectures without increasing the number of parameters
and with only a low cost on the throughput. The method code is based on PyTorch
and is available at this https URL

在这篇论文中，通过使用 DCLS 方法替代卷积架构中的 DSC 层，显著提高了音频标记的平均精度，同时不增加参数数量，并且对吞吐量只有较低的成本。