Compression of Acoustic Event Detection Models With Quantized Distillation
By Bowen Shi, Ming Sun, Chieh-Chi Kao, Viktor Rozgic, Spyros Matsoukas, Chao Wang. 2019
14 citations recorded by the source corpus.
Acoustic Event Detection (AED), aiming at detecting categories of events based on audio signals, has found application in many intelligent systems. Recently deep neural network significantly advances this field and reduces detection errors to a large scale. However how to efficiently execute deep models in AED has received much less attention. Meanwhile state-of-the-art AED models are based on large deep models, which are computational demanding and challenging to deploy on devices with constrained computational resources. In this paper, we present a simple yet effective compression approach which jointly leverages knowledge distillation and quantization to compress larger network (teacher model) into compact network (student model). Experimental results show proposed technique not only lowers error rate of original compact network by 15% through distillation but also further reduces its model size to a large extent (2% of teacher, 12% of full-precision student) through quantization.
Subfields: Artificial Intelligence, Signal Processing
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In Artificial Intelligence
- Dacheng Tao
- Jianping Gou
- S. Maybank
- Baosheng Yu
- Gabriella Csurka
- Jing Zhang
- Charu C. Aggarwal
- Dan Alistarh
In Signal Processing
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.”