Abstract
A noise-robust algorithm for segmentation of breath events during continuous speech is presented. The built-in microphone of a smartphone is used to capture the speech signal (voiced and breath frames) under conditions of a relatively noisy background. A template matching approach, using mel-cepstrograms, is adopted for constructing several similarity measurements to distinguish between breath and non-breath frames. Breath events will be used for lung function regression.
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References
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© 2014 Springer International Publishing Switzerland
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González, I., Carretón, C., Ochoa, S.F., Bravo, J. (2014). Towards a Non-intrusive Self-management System for Asthma Control Using Smartphones. In: Hervás, R., Lee, S., Nugent, C., Bravo, J. (eds) Ubiquitous Computing and Ambient Intelligence. Personalisation and User Adapted Services. UCAmI 2014. Lecture Notes in Computer Science, vol 8867. Springer, Cham. https://doi.org/10.1007/978-3-319-13102-3_9
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DOI: https://doi.org/10.1007/978-3-319-13102-3_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13101-6
Online ISBN: 978-3-319-13102-3
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