@ARTICLE{Qian_Kun_Teaching_2018,
 author={Qian Kun and Janott, Christoph and Zhang Zixing and Deng Jun and Baird, Alice and Heiser Clemens and Hohenhorst, Winfried and Herzog, Michael and Hemmert Werner and Schuller, Björn},
 volume={vol. 43},
 number={No 3},
 journal={Archives of Acoustics},
 pages={465-475},
 howpublished={online},
 year={2018},
 publisher={Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics},
 abstract={This paper proposes a comprehensive study on machine listening for localisation of snore sound excitation. Here we investigate the effects of varied frame sizes, and overlap of the analysed audio chunk for extracting low-level descriptors. In addition, we explore the performance of each kind of feature when it is fed into varied classifier models, including support vector machines, k-nearest neighbours, linear discriminant analysis, random forests, extreme learning machines, kernel-based extreme learning machines, multilayer perceptrons, and deep neural networks. Experimental results demonstrate that, wavelet packet transform energy can outperform most other features. A deep neural network trained with subband energy ratios reaches the highest performance achieving an unweighted average recall of 72.8% from four types for snoring.},
 type={Artykuły / Articles},
 title={Teaching Machines on Snoring: A Benchmark on Computer Audition for Snore Sound Excitation Localisation},
 URL={http://www.journals.pan.pl/Content/108118/PDF/123918.pdf},
 doi={10.24425/123918},
 keywords={snore sound, obstructive sleep apnea, acoustic features, machine learning},
}