Convolutional Recurrent Neural Networks and Acoustic Data Augmentation for Snore Detection

Pages: 35 - 46
Published: Jan 1, 2020
Abstract
In this paper, we propose an algorithm for snoring sounds detection based on convolutional recurrent neural networks (CRNN). The log Mel energy spectrum of the audio signal is extracted from overnight recordings and is used as input to the CRNN with the aim to detect the precise onset and offset time of the sound events. The dataset used in the experiments is highly imbalanced toward the non-snore class. A data augmentation technique is...
Paper Details
Title
Convolutional Recurrent Neural Networks and Acoustic Data Augmentation for Snore Detection
Published Date
Jan 1, 2020
Pages
35 - 46
Citation AnalysisPro
  • Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
  • Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.