Wyniki wyszukiwania

Filtruj wyniki

  • Czasopisma
  • Autorzy
  • Słowa kluczowe
  • Data
  • Typ

Wyniki wyszukiwania

Wyników: 2
Wyników na stronie: 25 50 75
Sortuj wg:


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.
Przejdź do artykułu

Ta strona wykorzystuje pliki 'cookies'. Więcej informacji