While efficient architectures and a plethora of augmentations for end-to-end image classification tasks have been suggested and heavily investigated, state-of-the-art techniques for audio classifications still rely on numerous representations of the audio signal together with large architectures, finetuned from large datasets. By utilizing the inherited lightweight nature of audio and novel audio augmentations, we were able to present an efficient end-to-end1 network with strong generalization ability. Experiments on a variety of sound classification sets demonstrate the effectiveness and robustness of our approach, by achieving state-of-the-art results in various settings. Public code will be available.

翻译：虽然提出了高效的架构和大量用于端到端图像分类任务的扩增结构,并对此进行了大量调查,但最先进的音频分类技术仍然依靠大量音频信号和大型结构的表述,并参照大型数据集进行微调。通过利用音频和新音频扩增所遗留的轻量级性质,我们得以展示一个高效端到端1的网络,具有很强的概括能力。对各种健全的分类组合的实验表明我们的方法的有效性和稳健性,通过在各种环境中取得最先进的结果,可以提供公共代码。