%0 Journal Article
	%A Katsumi Hirata
	%D 2019
	%J International Journal of Electronics and Communication Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 156, 2019
	%T Slice Bispectrogram Analysis-Based Classification of Environmental Sounds Using Convolutional Neural Network
	%U https://publications.waset.org/pdf/10010936
	%V 156
	%X 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.

	%P 742 - 745