Semi-supervised Acoustic Event Detection Based on Tri-training

By Bowen Shi, Ming Sun, Chieh-Chi Kao, Viktor Rozgic, Spyros Matsoukas, Chao Wang. 2019

19 citations recorded by the source corpus.

This paper presents our work of training acoustic event detection (AED) models using unlabeled dataset. Recent acoustic event detectors are based on large-scale neural networks, which are typically trained with huge amounts of labeled data. Labels for acoustic events are expensive to obtain, and relevant acoustic event audios can be limited, especially for rare events. In this paper we leverage an Internet-scale un-labeled dataset with potential domain shift to improve the detection of acoustic events. Based on the classic tri-training approach, our proposed method shows accuracy improvement over both the supervised training baseline, and semi-supervised self-training set-up, in all pre-defined acoustic event detection tasks. As our approach relies on ensemble models, we further show the improvements can be distilled to a single model via knowledge distillation, with the resulting single student model maintaining high accuracy of teacher ensemble models.

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jianping Gou
2. Dacheng Tao
3. Baosheng Yu
4. S. Maybank
5. Changxing Ding
6. Chao-qun Hong
7. Jing Zhang
8. Jun Yu

Citations in the corpus

• Knowledge Distillation: A Survey (2020, methodology): “…et al. 2018, 2019c, 2020), audio classification (Gao et al. 2019; Perez et al. 2020), text-independent speaker recognition (Ng et al. 2018), speech enhancement (Watanabe et al. 2017), acoustic event detection (Price et al. 2016; Shi et al. 2019a,b), speech synthesis (Oord et al. 2018) and so on.”