论文标题
优化卷积复发性神经网络的时间分辨率进行声音事件检测
Optimizing Temporal Resolution Of Convolutional Recurrent Neural Networks For Sound Event Detection
论文作者
论文摘要
在这份技术报告中，我们针对DCASE 2021挑战的子任务4提交的系统详细描述了有关声音事件检测的系统。这些模型与为此问题提供的基线密切相关，因为它们本质上是在平均教师环境中训练的卷积复发网络，以处理所提供的数据的异质注释。但是，预测的时间分辨率是针对以下事实，即使用两个基于相交的指标评估了这些系统，这些指标涉及时间定位，涉及不同需求。这是通过优化合并操作来完成的。 对于定义的评估方案的第一个，对时间定位准确性施加相对严格的要求，我们最佳模型在验证数据上达到了0.3609的PSDS分数。这仅比基线系统（0.342）获得的性能要好得多：基线网络中的合并量已经是最佳的，因此，没有进行实质性更改，从而解释了这一结果。 对于第二种评估方案，对本地化准确性施加相对宽松的限制，我们表现最佳的系统在验证数据上的PSDS得分为0.7312。这比基线模型（0.527）获得的性能要好得多，这可以有效地归因于应用于网络汇总操作的更改。
In this technical report, the systems we submitted for subtask 4 of the DCASE 2021 challenge, regarding sound event detection, are described in detail. These models are closely related to the baseline provided for this problem, as they are essentially convolutional recurrent neural networks trained in a mean teacher setting to deal with the heterogeneous annotation of the supplied data. However, the time resolution of the predictions was adapted to deal with the fact that these systems are evaluated using two intersection-based metrics involving different needs in terms of temporal localization. This was done by optimizing the pooling operations. For the first of the defined evaluation scenarios, imposing relatively strict requirements on the temporal localization accuracy, our best model achieved a PSDS score of 0.3609 on the validation data. This is only marginally better than the performance obtained by the baseline system (0.342): The amount of pooling in the baseline network already turned out to be optimal, and thus, no substantial changes were made, explaining this result. For the second evaluation scenario, imposing relatively lax restrictions on the localization accuracy, our best-performing system achieved a PSDS score of 0.7312 on the validation data. This is significantly better than the performance obtained by the baseline model (0.527), which can effectively be attributed to the changes that were applied to the pooling operations of the network.
