Snoring classified: The Munich-Passau Snore Sound Corpus. (1st March 2018)

• Record Type: Journal Article
• Authors: Janott, Christoph; Schmitt, Maximilian; Zhang, Yue; Qian, Kun; Pandit, Vedhas; Zhang, Zixing; Heiser, Clemens; Hohenhorst, Winfried; Herzog, Michael; Hemmert, Werner; Schuller, Björn
• Is Part Of: Computers in biology and medicine. Volume 94(2018)
• Page Start: 106
• Page End: 118
• Publication Date: 2018-03-01
• Subjects: Obstructive Sleep Apnea -- Primary snoring -- Snore sound classification -- Machine learning -- Drug-Induced Sleep Endoscopy; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2018.01.007
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Objective: Snoring can be excited in different locations within the upper airways during sleep. It was hypothesised that the excitation locations are correlated with distinct acoustic characteristics of the snoring noise. To verify this hypothesis, a database of snore sounds is developed, labelled with the location of sound excitation. Methods: Video and audio recordings taken during drug induced sleep endoscopy (DISE) examinations from three medical centres have been semi-automatically screened for snore events, which subsequently have been classified by ENT experts into four classes based on the VOTE classification. The resulting dataset containing 828 snore events from 219 subjects has been split into Train, Development, and Test sets. An SVM classifier has been trained using low level descriptors (LLDs) related to energy, spectral features, mel frequency cepstral coefficients (MFCC), formants, voicing, harmonic-to-noise ratio (HNR), spectral harmonicity, pitch, and microprosodic features. Results: An unweighted average recall (UAR) of 55.8% could be achieved using the full set of LLDs including formants. Best performing subset is the MFCC-related set of LLDs. A strong difference in performance could be observed between the permutations of train, development, and test partition, which may be caused by the relatively low number of subjects included in the smaller classes of the strongly unbalanced data set. Conclusion: A database of snoring sounds is presented which are classified according to their sound excitation location based on objective criteria and verifiable video material. With the database, it could be demonstrated that machine classifiers can distinguish different excitation location of snoring sounds in the upper airway based on acoustic parameters. Highlights: Snore sound excitation locations can be distinguished by acoustic properties. Automatic classification models based on speech-features prove successful. The ComParE feature set, used successfully in paralinguistics, showed best results. Mel Frequency Cepstral Coefficients (MFCCs) were the best-performing single subset. Formant-based features alone yielded inferior results.