Abstract
A general problem in acoustic scene classification task is the mismatched conditions between training and testing data, which significantly reduces the performance of the developed methods on classification accuracy. As a countermeasure, we present the first method of unsupervised adversarial domain adaptation for acoustic scene classification. We employ a model pre-trained on data from one set of conditions and by using data from other set of conditions, we adapt the model in order that its output cannot be used for classifying the set of conditions that input data belong to. We use a freely available dataset from the DCASE 2018 challenge Task 1, subtask B, that contains data from mismatched recording devices. We consider the scenario where the annotations are available for the data recorded from one device, but not for the rest. Our results show that with our model agnostic method we can achieve $\sim 10\%$ increase at the accuracy on an unseen and unlabeled dataset, while keeping almost the same performance on the labeled dataset.
Abstract (translated)
声学场景分类任务中的一般问题是训练和测试数据之间的不匹配条件，这显着降低了所开发方法对分类准确性的性能。作为对策，我们提出了第一种用于声学场景分类的无监督对抗域自适应方法。我们使用预先训练过一组条件数据的模型，并使用来自其他条件的数据，我们调整模型，使其输出不能用于对输入数据所属的条件集进行分类。我们使用DCASE 2018挑战任务1中的免费数据集，子任务B，其中包含来自不匹配记录设备的数据。我们考虑注释可用于从一个设备记录的数据的情况，但不考虑其余设备。我们的结果表明，使用我们的模型不可知方法，我们可以在看不见和未标记的数据集上实现$ \ sim 10 \％$的增加，同时在标记数据集上保持几乎相同的性能。
URL
https://arxiv.org/abs/1808.05777
