Recently, massive architectures based on Convolutional Neural Network (CNN) and self-attention mechanisms have become necessary for audio classification. While these techniques are state-of-the-art, these works' effectiveness can only be guaranteed with huge computational costs and parameters, large amounts of data augmentation, transfer from large datasets and some other tricks. By utilizing the lightweight nature of audio, we propose an efficient network structure called Paired Inverse Pyramid Structure (PIP) and a network called Paired Inverse Pyramid Structure MLP Network (PIPMN). The PIPMN reaches 96\% of Environmental Sound Classification (ESC) accuracy on the UrbanSound8K dataset and 93.2\% of Music Genre Classification (MGC) on the GTAZN dataset, with only 1 million parameters. Both of the results are achieved without data augmentation or model transfer. Public code is available at: https://github.com/JNAIC/PIPMN

翻译：近期，基于卷积神经网络（CNN）和自注意力机制的大规模架构已成为音频分类领域的必要手段。虽然这些技术达到了当前最优水平，但其有效性只能通过巨大的计算开销和参数量、大量的数据增强、大规模数据集迁移以及其他技巧得以保证。利用音频的轻量特性，我们提出了一种名为配对逆金字塔结构（PIP）的高效网络结构，以及基于该结构的配对逆金字塔结构MLP网络（PIPMN）。该网络在UrbanSound8K数据集上实现了96%的环境声音分类（ESC）准确率，在GTAZN数据集上实现了93.2%的音乐流派分类（MGC）准确率，且参数量仅为100万。这两项结果均未使用数据增强或模型迁移。公开代码可在以下地址获取：https://github.com/JNAIC/PIPMN