The S{\o}rensen--Dice Coefficient has recently seen rising popularity as a loss function (also known as Dice loss) due to its robustness in tasks where the number of negative samples significantly exceeds that of positive samples, such as semantic segmentation, natural language processing, and sound event detection. Conventional training of polyphonic sound event detection systems with binary cross-entropy loss often results in suboptimal detection performance as the training is often overwhelmed by updates from negative samples. In this paper, we investigated the effect of the Dice loss, intra- and inter-modal transfer learning, data augmentation, and recording formats, on the performance of polyphonic sound event detection systems with multichannel inputs. Our analysis showed that polyphonic sound event detection systems trained with Dice loss consistently outperformed those trained with cross-entropy loss across different training settings and recording formats in terms of F1 score and error rate. We achieved further performance gains via the use of transfer learning and an appropriate combination of different data augmentation techniques.

中文翻译：

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使用 Sørensen-Dice 系数损失和转移学习改进多声道录音的复音事件检测

S{\o}rensen--Dice Coefficient 最近作为一种损失函数（也称为 Dice 损失）越来越受欢迎，因为它在负样本数量显着超过正样本数量的任务中具有鲁棒性，例如语义分割、自然语言处理和声音事件检测。具有二进制交叉熵损失的和弦声音事件检测系统的常规训练通常会导致检测性能欠佳，因为训练经常被来自负样本的更新所淹没。在本文中，我们研究了 Dice 损失、模内和模间转移学习、数据增强和记录格式对具有多通道输入的和弦声音事件检测系统性能的影响。我们的分析表明，在 F1 分数和错误率方面，使用 Dice loss 训练的和弦声音事件检测系统在不同的训练设置和记录格式中始终优于使用交叉熵损失训练的系统。我们通过使用迁移学习和不同数据增强技术的适当组合实现了进一步的性能提升。