CaR-FOREST：用于重叠音频事件检测的联合分类-回归决策森林
CaR-FOREST: Joint Classification-Regression Decision Forests for Overlapping Audio Event Detection
摘要：
本报告介绍了我们提交的DCASE 2016挑战的任务2和任务3。该系统旨在处理连续流中重叠音频事件的检测，其中检测器基于随机决策森林。建议的森林同时进行分类和回归的联合训练。最初，训练是面向分类的，以鼓励树从重叠的混合物中选择有区别的特征，以将积极的音频片段与消极的音频片段分开。然后执行回归阶段以让肯定音频片段投票事件开始和偏移，并且因此对音频事件的时间结构进行建模。一个随机决策森林是专门为每个感兴趣的事件类别训练的。开发数据的实验结果表明，我们的系统显着优于基线的任务2的评估，而他们是劣于基线的任务3的评估。
英文摘要：
This report describes our submissions to Task2 and Task3 of the DCASE 2016 challenge. The systems aim at dealing with the detection of overlapping audio events in continuous streams, where the detectors are based on random decision forests. The proposed forests are jointly trained for classification and regression simultaneously. Initially, the training is classification-oriented to encourage the trees to select discriminative features from overlapping mixtures to separate positive audio segments from the negative ones. The regression phase is then carried out to let the positive audio segments vote for the event onsets and offsets, and therefore model the temporal structure of audio events. One random decision forest is specifically trained for each event category of interest. Experimental results on the development data show that our systems significantly outperform the baseline on the Task2 evaluation while they are inferior to the baseline in the Task3 evaluation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
