title: Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments.
abstract: Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that do not generalize well. In this paper, we make an efficient combination of confidence-based Active Learning and Self-Training with the aim of minimizing the need for human annotation for sound classification model training. The proposed method pre-processes the instances that are ready for labeling by calculating their classifier confidence scores, and then delivers the candidates with lower scores to human annotators, and those with high scores are automatically labeled by the machine. We demonstrate the feasibility and efficacy of this method in two practical scenarios: pool-based and stream-based processing. Extensive experimental results indicate that our approach requires significantly less labeled instances to reach the same performance in both scenarios compared to Passive Learning, Active Learning and Self-Training. A reduction of 52.2% in human labeled instances is achieved in both of the pool-based and stream-based scenarios on a sound classification task considering 16,930 sound instances.
authors.affiliation: Language Computing Lab, Samsung R&D Institute of China - Beijing (SRC-B), Beijing, China.
authors.forename: Wenjing
authors.initials: W
authors.lastname: Han
authors.affiliation: Department of Music, University of Liverpool, Liverpool, United Kingdom.|Department of Computing, Imperial College London, London, United Kingdom.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Coutinho
authors.affiliation: Department of Computer Science and Technology, Tsinghua University, Beijing, China.
authors.forename: Huabin
authors.identifier: http://orcid.org/0000-0002-0507-5568
authors.initials: H
authors.lastname: Ruan
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
authors.forename: Haifeng
authors.initials: H
authors.lastname: Li
authors.affiliation: Department of Computing, Imperial College London, London, United Kingdom.|School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.|Complex Systems Engineering, University of Passau, Passau, Germany.
authors.forename: Björn
authors.initials: B
authors.lastname: Schuller
authors.affiliation: Language Computing Lab, Samsung R&D Institute of China - Beijing (SRC-B), Beijing, China.
authors.forename: Xiaojie
authors.initials: X
authors.lastname: Yu
authors.affiliation: Language Computing Lab, Samsung R&D Institute of China - Beijing (SRC-B), Beijing, China.
authors.forename: Xuan
authors.initials: X
authors.lastname: Zhu
mesh_terms: D000162:Acoustics; D000465:Algorithms; D002965:Classification; D006801:Humans; D000069550:Machine Learning; D018794:Problem-Based Learning / Q000379:methods; D013016:Sound; D000069553:Supervised Machine Learning