ttp://iaeme.com/Home/journal/IJCET 33 editor@iaeme.com h International Journal of Computer Engineering & Technology (IJCET) Volume 9, Issue 4, July-Aug 2018, pp. 33–48, Article IJCET_09_04_004 Available online at ttp://iaeme.com/Home/issue/IJCET?Volume=9&Issue=4 h Journal Impact Factor (2016): 9.3590(Calculated by GISI) www.jifactor.com ISSN Print: 0976-6367 and ISSN Online: 0976–6375 © IAEME Publication CONTENT BASED AUDIO CLASSIFICATION USING ARTIFICIAL NEURAL NETWORK TECHNIQUES K.Karthikeyan Research Scholar, Department of Compute Science, Marudupandiyar College, Thanjavur, Tamilnadu, India Dr.R.Mala Assistant Professor, Department of Computer Science, Marudupandiyar College, Thanjavur, Tamilnadu, India ABSTRACT Audio signals which include speech, music and environmental sounds are important types of media. The problem of distinguishing audio signals into these different audio types is thus becoming increasingly significant. A human listener can easily distinguish between different audio types by just listening to a short segment of an audio signal. However, solving this problem using computers has proven to be very difficult. Nevertheless, many systems with modest accuracy could still be implemented. The experimental results demonstrate the effectiveness of our classification system. The complete system is developed in ANN Techniques with Autonomic Computing system. Key words: MFCC, ANN, Knowledge Base, Learning Process, Energy, Audio feature extraction. Cite this Article: K.Karthikeyan and Dr.R.Mala, Content Based Audio Classification Using Artificial Neural Network Techniques. International Journal of Computer Engineering & Technology, 9(4), 2018, pp. 33–48. ttp://iaeme.com/Home/issue/IJCET?Volume=9&Issue=4 h 1. INTRODUCTION Audio segmentation and classification have applications in wide areas. For instance, content based audio classification and retrieval is broadly used in the entertainment industry, audio archive management, commercial music usage, surveillance, etc. There are many digital audio databases on the World Wide Web nowadays; here audio segmentation and classification would be needed for audio searching and indexing. Recently, there has been a great deal of interest in monitoring broadcast news programs, in this case classification of speech data in terms of speaker could help in efficient navigation through broadcast news archives. In music psychology and music education, emotions based components of music has been recognized as the most strongly component associated with music expressivity. Music