Abstract
This paper describes an acoustic scene classification method which achieved the 4th ranking result in the IEEE AASP challenge of Detection and Classification of Acoustic Scenes and Events 2016. In order to accomplish the ensuing task, several methods are explored in three aspects: feature extraction, feature transformation, and score fusion for final decision. In the part of feature extraction, several features are investigated for effective acoustic scene classification. For resolving the issue that the same sound can be heard in different places, a feature transformation is applied for better separation for classification. From these, several systems based on different feature sets are devised for classification. The final result is determined by fusing the individual systems. The method is demonstrated and validated by the experiment conducted using the Challenge database.
Abstract (translated)
本文描述了一种声学场景分类方法,该方法在2016年声学场景和事件检测与分类的IEEE AASP挑战中取得了第四的排名结果。为了完成随后的任务,从三个方面探索了几种方法:特征提取,特征。转换,并为最终决定进行得分融合。在特征提取部分,研究了几个有效声学场景分类的特征。为了解决在不同位置可以听到相同声音的问题,应用特征变换以更好地分离以进行分类。由此,设计了基于不同特征集的若干系统用于分类。最终结果是通过融合各个系统来确定的。使用Challenge数据库进行的实验证明并验证了该方法。
URL
https://arxiv.org/abs/1807.04970