Automatic segmentation of infant cry signals using hidden Markov models

Gaurav Naithani, Jaana Kivinummi, Tuomas Virtanen, Outi Tammela, Mikko J. Peltola, Jukka M. Leppänen

    Research output: Contribution to journalArticleScientificpeer-review

    11 Citations (Scopus)
    29 Downloads (Pure)


    Automatic extraction of acoustic regions of interest from recordings captured in realistic clinical environments is a necessary preprocessing step in any cry analysis system. In this study, we propose a hidden Markov model (HMM) based audio segmentation method to identify the relevant acoustic parts of the cry signal (i.e., expiratory and inspiratory phases) from recordings made in natural environments with various interfering acoustic sources. We examine and optimize the performance of the system by using different audio features and HMM topologies. In particular, we propose using fundamental frequency and aperiodicity features. We also propose a method for adapting the segmentation system trained on acoustic material captured in a particular acoustic environment to a different acoustic environment by using feature normalization and semi-supervised learning (SSL). The performance of the system was evaluated by analyzing a total of 3 h and 10 min of audio material from 109 infants, captured in a variety of recording conditions in hospital wards and clinics. The proposed system yields frame-based accuracy up to 89.2%. We conclude that the proposed system offers a solution for automated segmentation of cry signals in cry analysis applications.

    Original languageEnglish
    JournalEurasip Journal on Audio, Speech, and Music Processing
    Issue number1
    Publication statusPublished - 2018
    Publication typeA1 Journal article-refereed


    • Acoustic analysis
    • Audio segmentation
    • Hidden Markov models
    • Infant cry analysis
    • Model adaptation

    Publication forum classification

    • Publication forum level 1


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