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- Title
Transfer Learning for Improved Audio-Based Human Activity Recognition.
- Authors
Ntalampiras, Stavros; Potamitis, Ilyas
- Abstract
Human activities are accompanied by characteristic sound events, the processing of which might provide valuable information for automated human activity recognition. This paper presents a novel approach addressing the case where one or more human activities are associated with limited audio data, resulting in a potentially highly imbalanced dataset. Data augmentation is based on transfer learning; more specifically, the proposed method: (a) identifies the classes which are statistically close to the ones associated with limited data; (b) learns a multiple input, multiple output transformation; and (c) transforms the data of the closest classes so that it can be used for modeling the ones associated with limited data. Furthermore, the proposed framework includes a feature set extracted out of signal representations of diverse domains, i.e., temporal, spectral, and wavelet. Extensive experiments demonstrate the relevance of the proposed data augmentation approach under a variety of generative recognition schemes.
- Subjects
HUMAN activity recognition; ACOUSTICS; HIDDEN Markov models
- Publication
Biosensors (2079-6374), 2018, Vol 8, Issue 3, p60
- ISSN
2079-6374
- Publication type
Article
- DOI
10.3390/bios8030060