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- W3119528079 abstract "Breathing is essential to human life. Observing respiratory rate is an important part of physical examinations. Recognizing changes in breathing patterns plays a vital role in disease detection. This paper proposes a method of recognizing breathing sounds. Mel-frequency cepstral coefficients (MFCCs) and their first and second-order derivatives were extracted from the signals and used to create Hidden Markov Models (HMMs). The HMMs were used to model the acoustic phenomena of breathing signals and their changes. A Viterbi-based decoder used the HMMs and the breathing grammar to recognize breathing sounds. The results showed that using breathing grammar and a suitable distance of a microphone away from the nose could enhance the recognition accuracy. The time spent on processing the whole breathing sequence was less than half a second, which was much faster than using manual classification." @default.
- W3119528079 created "2021-01-18" @default.
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- W3119528079 date "2020-10-21" @default.
- W3119528079 modified "2023-10-02" @default.
- W3119528079 title "Recognizing Breathing Sounds Using HMMs and Grammar" @default.
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- W3119528079 doi "https://doi.org/10.1109/incit50588.2020.9310966" @default.
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