- Tytuł:
- Teaching Machines on Snoring : A Benchmark on Computer Audition for Snore Sound Excitation Localisation
- Autorzy:
-
Qian, K.
Janott, C.
Zhang, Z.
Deng, J.
Baird, A.
Heiser, C.
Hohenhorst, W.
Herzog, M.
Hemmert, W.
Schuller, B. - Powiązania:
- https://bibliotekanauki.pl/articles/177964.pdf
- Data publikacji:
- 2018
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
snore sound
obstructive sleep apnea
acoustic features
machine learning - Opis:
- 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.
- Źródło:
-
Archives of Acoustics; 2018, 43, 3; 465-475
0137-5075 - Pojawia się w:
- Archives of Acoustics
- Dostawca treści:
- Biblioteka Nauki
Artykuł