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Health Monitoring Using Deep Learning Of Acoustic And Speech Signals, Eric E. Hamke
Health Monitoring Using Deep Learning Of Acoustic And Speech Signals, Eric E. Hamke
Electrical and Computer Engineering ETDs
The focus of the research is to identify stress markers in a firefighter's speech. These markers include changes in breathing patterns and changes in the fundamental frequency of an individual’s voice. The breathing patterns are characterized using the number of breaths taken in a minute and the time spent inhaling. These measures are estimated using a Restricted Boltzmann Machine to process a firefighters’ SCBA regulator sounds, as open and closed. The classifications are then combined into continuous intervals. Observing the length of the intervals and the number of interval-starts represents time spent inhaling and the breathing rates (breaths per minute). …