This paper presents a domain adaptation model for sound event detection. A common challenge for sound event detection is how to deal with the mismatch among different datasets. Typically, the performance of a model will decrease if it is tested on a dataset which is different from the one that the model is trained on. To address this problem, based on convolutional recurrent neural networks (CRNNs), we propose an adapted CRNN (A-CRNN) as an unsupervised adversarial domain adaptation model for sound event detection. We have collected and annotated a dataset in Singapore with two types of recording devices to complement existing datasets in the research community, especially with respect to domain adaptation. We perform experiments on recordings from different datasets and from different recordings devices. Our experimental results show that the proposed A-CRNN model can achieve a better performance on an unseen dataset in comparison with the baseline non-adapted CRNN model.

To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convolutional neural networks (CNNs) is proposed. Our two-stage system leverages on an ad-hoc score combination based on two CNN classifiers: (i) the first CNN classifies acoustic inputs into one of three broad classes, and (ii) the second CNN classifies the same inputs into one of ten finergrained classes. Three different CNN architectures are explored to implement the two-stage classifiers, and a frequency sub-sampling scheme is investigated. Moreover, novel data augmentation schemes for ASC are also investigated. Evaluated on DCASE 2020 Task 1a, our results show that the proposed ASC system attains a state-of-theart accuracy on the development set, where our best system, a twostage fusion of CNN ensembles, delivers a 81.9% average accuracy among multi-device test data, and it obtains a significant improvement on unseen devices. Finally, neural saliency analysis with class activation mapping (CAM) gives new insights on the patterns learnt by our models.

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