Concept drift adaptation for audio scene classification using high-level features
Author:
Affiliation:
1. Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University,Okayama,Japan
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9730107/9730121/09730332.pdf?arnumber=9730332
Reference18 articles.
1. A survey on feature drift adaptation: Definition, benchmark, challenges and future directions
2. Learning from streaming data with concept drift and imbalance: an overview
3. Acoustic scene classification using deep CNN with fine-resolution feature
4. Maximum Likelihood from Incomplete Data via the EM Algorithm;dempster;J R Stat Soc Ser B,1977
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