Sounds carry an abundance of information about activities and events in our everyday environment, such as traffic noise, road works, music, or people talking. Recent machine learning methods, such as convolutional neural networks (CNNs), have been shown to be able to automatically recognize sound activities, a task known as audio tagging. One such method, pre-trained audio neural networks (PANNs), provides a neural network which has been pre-trained on over 500 sound classes from the publicly available AudioSet dataset, and can be used as a baseline or starting point for other tasks. However, the existing PANNs model has a high computational complexity and large storage requirement. This could limit the potential for deploying PANNs on resource-constrained devices, such as on-the-edge sound sensors, and could lead to high energy consumption if many such devices were deployed. In this paper, we reduce the computational complexity and memory requirement of the PANNs model by taking a pruning approach to eliminate redundant parameters from the PANNs model. The resulting Efficient PANNs (E-PANNs) model, which requires 36\% less computations and 70\% less memory, also slightly improves the sound recognition (audio tagging) performance. The code for the E-PANNs model has been released under an open source license.

翻译：声音携带着关于我们日常环境中活动和事件的丰富信息，例如交通噪音、道路施工、音乐或人声对话。最近的机器学习方法（如卷积神经网络）已被证明能够自动识别声音活动，这一任务被称为音频标注。其中一种方法是预训练音频神经网络（PANNs），它提供了基于公开AudioSet数据集上超过500个声音类别进行预训练的神经网络，可作为其他任务的基线或起点。然而，现有的PANNs模型计算复杂度高且存储需求大，这可能限制其在资源受限设备（如边缘声音传感器）上的部署潜力，并可能导致大规模部署时的高能耗。本文通过采用剪枝方法消除PANNs模型中的冗余参数，降低了其计算复杂度和内存需求。由此产生的Efficient PANNs（E-PANNs）模型在减少36%计算量和70%内存占用的同时，还略微提升了声音识别（音频标注）性能。E-PANNs模型的代码已根据开源许可协议发布。