使用卷积神经网络的城市声音标签

在本文中，我们提出了一个在低数据环境下（每类少于100个带标签的示例）进行环境声音分类的框架。我们表明，使用经过预训练的图像分类模型以及数据增强技术的使用会比其他方法产生更高的性能。..

Urban Sound Tagging using Convolutional Neural Networks

In this paper, we propose a framework for environmental sound classification in a low-data context (less than 100 labeled examples per class). We show that using pre-trained image classification models along with the usage of data augmentation techniques results in higher performance over alternative approaches.We applied this system to the task of Urban Sound Tagging, part of the DCASE 2019. The objective was to label different sources of noise from raw audio data. A modified form of MobileNetV2, a convolutional neural network (CNN) model was trained to classify both coarse and fine tags jointly. The proposed model uses log-scaled Mel-spectrogram as the representation format for the audio data. Mixup, Random erasing, scaling, and shifting are used as data augmentation techniques. A second model that uses scaled labels was built to account for human errors in the annotations. The proposed model achieved the first rank on the leaderboard with Micro-AUPRC values of 0.751 and 0.860 on fine and coarse tags, respectively.