论文标题
与神经标签嵌入的关系教师学生学习，以适应声学场景的设备适应
Relational Teacher Student Learning with Neural Label Embedding for Device Adaptation in Acoustic Scene Classification
论文作者
论文摘要
在本文中，我们提出了一个域适应框架，以解决在嵌入神经标签（NLE）和关系教师学生学习（RTSL）的声学场景分类中的设备不匹配问题。考虑到声学场景类别之间的结构关系，我们提出的框架捕获了这种本质上无关的关系。在训练阶段，可转移的知识与源域中的NLE凝结。在适应阶段，采用了一种新颖的RTSL策略来学习适应的目标模型，而无需使用传统教师学习中经常需要的配对源目标数据。提出的框架将在Dcase 2018 Task1b数据集上评估。基于Alexnet-L深层分类模型的实验结果证实了我们提出的对不匹配情况的方法的有效性。 NLO-NLO-NO-NO-AROS适应与常规设备适应和基于教师学生的适应技术相比。 NLE与RTSL进一步提高了分类精度。
In this paper, we propose a domain adaptation framework to address the device mismatch issue in acoustic scene classification leveraging upon neural label embedding (NLE) and relational teacher student learning (RTSL). Taking into account the structural relationships between acoustic scene classes, our proposed framework captures such relationships which are intrinsically device-independent. In the training stage, transferable knowledge is condensed in NLE from the source domain. Next in the adaptation stage, a novel RTSL strategy is adopted to learn adapted target models without using paired source-target data often required in conventional teacher student learning. The proposed framework is evaluated on the DCASE 2018 Task1b data set. Experimental results based on AlexNet-L deep classification models confirm the effectiveness of our proposed approach for mismatch situations. NLE-alone adaptation compares favourably with the conventional device adaptation and teacher student based adaptation techniques. NLE with RTSL further improves the classification accuracy.
