论文标题
通过知识传输，数据增强和预处理，在小数据集上最大化音频事件检测模型的性能：一项消融研究
Maximizing Audio Event Detection Model Performance on Small Datasets Through Knowledge Transfer, Data Augmentation, And Pretraining: An Ablation Study
论文作者
论文摘要
X受体模型通过从Imagenet重量，预处理音频集和一条即时的数据增强管道的知识转移来达到ESC-50数据集上的最新（SOTA）精度，以进行音频事件检测。本文提出了一项消融研究，该研究分析了组件有助于提高性能和训练时间。还提出了一个较小的Xception模型，该模型接近SOTA性能，几乎三分之一的参数。
An Xception model reaches state-of-the-art (SOTA) accuracy on the ESC-50 dataset for audio event detection through knowledge transfer from ImageNet weights, pretraining on AudioSet, and an on-the-fly data augmentation pipeline. This paper presents an ablation study that analyzes which components contribute to the boost in performance and training time. A smaller Xception model is also presented which nears SOTA performance with almost a third of the parameters.
