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Biosensors 2018, 8(3), 60;

Transfer Learning for Improved Audio-Based Human Activity Recognition

Music Informatics Laboratory, Department of Computer Science, Università degli Studi di Milano, via Comelico 39, 20135, Milan, Italy
Technological Educational Institute of Crete, E. Daskalaki, Perivolia, 74100, Rethymno, Greece
Author to whom correspondence should be addressed.
Received: 29 May 2018 / Revised: 14 June 2018 / Accepted: 21 June 2018 / Published: 25 June 2018
(This article belongs to the Special Issue Smart Biomedical Sensors)
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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. View Full-Text
Keywords: transfer learning; generalized audio recognition; multidomain features; hidden Markov model; echo state network transfer learning; generalized audio recognition; multidomain features; hidden Markov model; echo state network

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Ntalampiras, S.; Potamitis, I. Transfer Learning for Improved Audio-Based Human Activity Recognition. Biosensors 2018, 8, 60.

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