{"title":"Slice Bispectrogram Analysis-Based Classification of Environmental Sounds Using Convolutional Neural Network","authors":"Katsumi Hirata","volume":156,"journal":"International Journal of Electronics and Communication Engineering","pagesStart":742,"pagesEnd":746,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10010936","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\u201150 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>\r\n","references":"[1]\tS. Chu, S. Narayanan and C.-C. J. Kuo, \u201cEnvironmental sound recognition with time\u2013frequency audio features,\u201d IEEE Transactions on Audio, Speech, and Language Processing, 17-6, pp.1142-1158, Aug. 2009.\r\n[2]\tF. Su, L. Yang, T. Lu and G. Wang, \u201cEnvironmental sound classification for scene recognition using local discriminant bases and HMM,\u201d Proceedings of the 19th ACM international conference on Multimedia, pp.1389-1392, Nov. 2011.\r\n[3]\tS. Chachada and C.-C. J. Kuo, \u201cEnvironmental sound recognition: A survey,\u201d 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, pp.1-9, Oct. 2013.\r\n[4]\tK. J. Piczak, \u201cEnvironmental sound classification with convolutional neural networks,\u201d 2015 IEEE international workshop on machine learning for signal processing, Sept. 2015.\r\n[5]\tM. Huzaifah, \u201cComparison of time-frequency representations for environmental sound classification using convolutional neural networks,\u201d ArXiv Prepr. ArXiv170607156, 2017.\r\n[6]\t\u201cESC-50: Dataset for Environmental Sound Classification\u201d, https:\/\/ github.com\/karoldvl\/ESC-50 (Last accessed at Oct. 3, 2019).\r\n[7]\tK. J. Piczak, \"ESC: Dataset for Environmental Sound Classification,\" Proceedings of the 23rd Annual ACM Conference on Multimedia, pp.1015-1018, Oct. 2015.\r\n[8]\tC. L. Nikias and A. P. Petropulu, Higher-order spectra analysis: a nonlinear signal processing framework, Prentice Hall, 1993, pp.7-30\r\n[9]\tV. Swarnkar, U. Abeyratne, and C. Hukins, \u201cObjective measure of sleepiness and sleep latency via bispectrum analysis of EEG,\u201d Medical and & biological engineering & computing, 48, pp.1203-1213, Dec. 2010.\r\n[10]\tK. Hirata, \u201cEstimating 3D-Position of A Stationary Random Acoustic Source Using Bispectral Analysis of 4-Point Detected Signals,\u201d International Journal of Computer and Information Engineering, 8-6, pp.932-935, 2014.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 156, 2019"}