Transfer Learning for Improved Audio-Based Human Activity Recognition
AbstractHuman 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. View Full-Text
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Ntalampiras, S.; Potamitis, I. Transfer Learning for Improved Audio-Based Human Activity Recognition. Biosensors 2018, 8, 60.
Ntalampiras S, Potamitis I. Transfer Learning for Improved Audio-Based Human Activity Recognition. Biosensors. 2018; 8(3):60.Chicago/Turabian Style
Ntalampiras, Stavros; Potamitis, Ilyas. 2018. "Transfer Learning for Improved Audio-Based Human Activity Recognition." Biosensors 8, no. 3: 60.
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