Teaching Machines on Snoring: A Benchmark on Computer Audition for Snore Sound Excitation Localisation
Kun Qian, Christoph Janott, Zhang Zixing, Deng Jun, Alice Baird, Clemens Heiser, Winfried Hohenhorst, Michael Herzog, Werner Hemmert, Björn Wolfgang Schuller · Archives of Acoustics · 2018
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.