@article{(Open Science Index):https://publications.waset.org/pdf/10010936,
	  title     = {Slice Bispectrogram Analysis-Based Classification of Environmental Sounds Using Convolutional Neural Network},
	  author    = {Katsumi Hirata},
	  country	= {},
	  institution	= {},
	  abstract     = {Certain systems can function well only if they recognize the sound environment as humans do. In this research, we focus on sound classification by adopting a convolutional neural network and aim to develop a method that automatically classifies various environmental sounds. Although the neural network is a powerful technique, the performance depends on the type of input data. Therefore, we propose an approach via a slice bispectrogram, which is a third-order spectrogram and is a slice version of the amplitude for the short-time bispectrum. This paper explains the slice bispectrogram and discusses the effectiveness of the derived method by evaluating the experimental results using the ESC‑50 sound dataset. As a result, the proposed scheme gives high accuracy and stability. Furthermore, some relationship between the accuracy and non-Gaussianity of sound signals was confirmed.
},
	    journal   = {International Journal of Electronics and Communication Engineering},
	  volume    = {13},
	  number    = {12},
	  year      = {2019},
	  pages     = {742 - 745},
	  ee        = {https://publications.waset.org/pdf/10010936},
	  url   	= {https://publications.waset.org/vol/156},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 156, 2019},
	}