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.
CITATION STYLE
González, I., Carretón, C., Ochoa, S. F., & Bravo, J. (2014). Towards a non-intrusive self-management system for asthma control using smartphones. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8867, 44–47. https://doi.org/10.1007/978-3-319-13102-3_9
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