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Breathing is an essential part of human survival, which carries informationabout a person's physiological and psychological state. Generally, breathboundaries are marked by experts before using for any task. An unsupervisedalgorithm for breath boundary detection has been proposed for breath soundsrecorded at the mouth also referred as vocal breath sounds (VBS) in this work.Breath sounds recorded at the mouth are used in this work because they are easyand contactless to record than tracheal breath sounds and lung breath sounds.The periodic nature of breath signal energy is used to segment the breathboundaries. Dynamic programming with the prior information of the number ofbreath phases($P$) and breath phase duration($d$) is used to find theboundaries. In this work, 367 breath boundaries from 60 subjects (31 healthy,29 patients) having 307 breaths are predicted. With the proposed method, M($89\%$), I ($13\%$), D ($11\%$) and S ($79\%$) is found. The proposed methodshows better performance than the baselines used in this work. Even theclassification performance between asthmatic and healthy subjects usingestimated boundaries by the proposed method is comparable with the ground truthboundaries.

An unsupervised segmentation of vocal breath sounds