Semi-supervised active learning for sound classification in hybrid learning environments

Wenjing Han, Eduardo Coutinho, Huabin Ruan, Haifeng Li, Björn Schuller, Xiaojie Yu, Xuan Zhu

Research output: Contribution to journalArticlepeer-review

56 Scopus citations

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.

Original languageEnglish
Article numbere0162075
JournalPLoS ONE
Volume11
Issue number9
DOIs
StatePublished - Sep 2016
Externally publishedYes

Fingerprint

Dive into the research topics of 'Semi-supervised active learning for sound classification in hybrid learning environments'. Together they form a unique fingerprint.

Cite this