Designing Acoustic Scene Classification Models with CNN Variants Technical
Designing Acoustic Scene Classification Models with CNN Variants Technical
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发表时间：
2020
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通讯作者：
Sangwon Suh;Sooyoung Park;Youngho Jeong;Taejin Lee
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作者：
Sangwon Suh;Sooyoung Park;Youngho Jeong;Taejin Lee
本技术报告描述了我们针对DCASE2020挑战任务1的声场分类系统。对子任务A，我们设计了一个由三个并行ResNet实现的单一模型，称为三叉戟ResNet。我们已经证实，这种结构在分析从少数或看不见的设备收集的样本时是有益的，并确认了测试拆分的73.7%的分类准确率。对于子任务B，我们使用初始模块构建了一个名为浅初始的模型，该模型具有比DCASE基线系统的CNN更少的参数。由于先启模块的稀疏结构，我们将模型的准确率提高到97.6%，同时减少了参数的数量。
This technical report describes our Acoustic Scene Classification systems for DCASE2020 challenge Task1. For subtask A, we designed a single model implemented with three parallel ResNets, which is named Trident ResNet. We have confirmed that this structure is beneficial when analyzing samples collected from minority or unseen devices, and confirmed 73.7% classification accuracy for the test split. For subtask B, we used the Inception module to build a model named Shallow Inception that has fewer parameters than the CNN of the DCASE baseline system. Due to the sparse structure of the Inception module, we have enhanced the accuracy of the model up to 97.6%, while reducing the number of parameters.