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Title:

[VOTE versus ACLTE: comparison of two snoring noise classifications using machine learning methods].

Document type:
Journal Article
Author(s):
Janott, C; Schmitt, M; Heiser, C; Hohenhorst, W; Herzog, M; Carrasco Llatas, M; Hemmert, W; Schuller, B
Abstract:
BACKGROUND: Acoustic snoring sound analysis is a noninvasive method for diagnosis of the mechanical mechanisms causing snoring that can be performed during natural sleep. The objective of this work is development and evaluation of classification schemes for snoring sounds that can provide meaningful diagnostic support. MATERIALS AND METHODS: Based on two annotated snoring noise databases with different classifications (s-VOTE with four classes versus ACLTE with five classes), identically structu...     »
Journal title abbreviation:
HNO
Year:
2019
Journal volume:
67
Journal issue:
9
Pages contribution:
670-678
Fulltext / DOI:
doi:10.1007/s00106-019-0696-5
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/31190193
Print-ISSN:
0017-6192
TUM Institution:
Hals-Nasen-Ohrenklinik und Poliklinik
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