@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}, }