Automatic extraction of acoustic regions of interest from recordings captured in realistic clinical environments is a\nnecessary preprocessing step in any cry analysis system. In this study, we propose a hidden Markov model (HMM)\nbased audio segmentation method to identify the relevant acoustic parts of the cry signal (i.e., expiratory and\ninspiratory phases) from recordings made in natural environments with various interfering acoustic sources. We\nexamine and optimize the performance of the system by using different audio features and HMM topologies. In\nparticular, we propose using fundamental frequency and aperiodicity features. We also propose a method for\nadapting the segmentation system trained on acoustic material captured in a particular acoustic environment to a\ndifferent acoustic environment by using feature normalization and semi-supervised learning (SSL). The performance\nof the system was evaluated by analyzing a total of 3 h and 10 min of audio material from 109 infants, captured in a\nvariety of recording conditions in hospital wards and clinics. The proposed system yields frame-based accuracy up to\n89.2%. We conclude that the proposed system offers a solution for automated segmentation of cry signals in cry\nanalysis applications
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