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JBE, vol. 23, no. 6, pp.855-865, November, 2018
Dual CNN Structured Sound Event Detection Algorithm Based on Real Life Acoustic Dataset
Sangwon Suh, Wootaek Lim, Youngho Jeong, Taejin Lee, and Hui Yong Kim
C.A E-mail: firstname.lastname@example.org
Sound event detection is one of the research areas to model human auditory cognitive characteristics by recognizing events in an environment with multiple acoustic events and determining the onset and offset time for each event. DCASE, a research group on acoustic scene classification and sound event detection, is proceeding challenges to encourage participation of researchers and to activate sound event detection research. However, the size of the dataset provided by the DCASE Challenge is relatively small compared to ImageNet, which is a representative dataset for visual object recognition, and there are not many open sources for the acoustic dataset. In this study, the sound events that can occur in indoor and outdoor are collected on a larger scale and annotated for dataset construction. Furthermore, to improve the performance of the sound event detection task, we developed a dual CNN structured sound event detection system by adding a supplementary neural network to a convolutional neural network to determine the presence of sound events. Finally, we conducted a comparative experiment with both baseline systems of the DCASE 2016 and 2017.
Keyword: Machine learning, Deep learning, Audio signal processing, Sound event detection, Dataset
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