Snoring classified: The Munich-Passau Snore Sound Corpus

Christoph Janott, Maximilian Schmitt, Yue Zhang, Kun Qian, Vedhas Pandit, Zixing Zhang, Clemens Heiser, Winfried Hohenhorst, Michael Herzog, Werner Hemmert, Björn Schuller

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

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.

Original languageEnglish
Pages (from-to)106-118
Number of pages13
JournalComputers in Biology and Medicine
Volume94
DOIs
StatePublished - 1 Mar 2018

Keywords

  • Drug-Induced Sleep Endoscopy
  • Machine learning
  • Obstructive Sleep Apnea
  • Primary snoring
  • Snore sound classification

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