论文标题
软木选择：一种自动功能平滑方法用于声音事件检测
Soft-Median Choice: An Automatic Feature Smoothing Method for Sound Event Detection
论文作者
论文摘要
在声音事件检测系统（SED）系统中，由于几个问题，在培训期间从未在训练过程中优化后期处理的中间过滤器的长度。长度不收到梯度，因此在后传播过程中无法学习。模型中插入的中间过滤还会导致梯度流动中的块，而平滑过程通过忽略误差误导了模型。为了解决这些问题，我们提供了不同的功能渠道以及原始功能，因此可以在认识所有错误的同时优化权重。然后，我们使用线性层来整合结果并产生线性组合。我们进一步设计软中函数以驱散梯度流。所提出的框架称为软中心选择（SMC）。实验表明，SMC块不仅会根据训练集自动平滑功能，还迫使模型提取声音事件的所有帧共享的共同特征。在验证和评估集中，所提出的方法的性能优于基线的基线超过10％的F1得分（EBF），并且略高于最先进的SED系统的单个模型。
In Sound Event Detection (SED) systems, the lengths of median filters for post-processing have never been optimized during training due to several problems. No gradient is received by the lengths so they cannot be learned during back-propagation. The median-filtering inserted in the models also causes block in gradient flowing and the smoothing process misleads the model by ignoring errors. To resolve these problems, we provide different channels of features smoothed to different extents along with the original feature, so the model can optimize the weights while cognizing all the errors. We then use a linear layer to integrate the results and produce a linear combination. We further design the soft-median function to dredge the gradient flow. The proposed framework is called Soft-Median Choice (SMC). Experiments show that the SMC block not only automatically smooths the features based on the training set, but also forces the model to extract common features shared by all the frames of a sound event. The performance of the proposed method outperforms the baseline by over 10% of Event-Based F1 Score (EBFS) in both the validation and the evaluation set, and also slightly outperforms the single model of the state-of-the-art SED system.
