CONTENT BASED AUDIO CLASSIFICATION USING ARTIFICIAL NEURAL NETWORK TECHNIQUES
Journal: JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (JCET) (Vol.9, No. 4)Publication Date: 2018-08-27
Authors : K.KARTHIKEYAN; Dr.R.MALA;
Page : 33-48
Keywords : MFCC; ANN; Knowledge Base; Learning Process; Energy; Audio feature extraction.;
Abstract
Pes 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.
Other Latest Articles
- SUPRA PAIRWISE CONNECTED AND PAIRWISE SEMI-CONNECTED SPACES
- ROUGH SET THEORY APPLICATIONS ON MEASURING TEXT MINING TASKS
- SETUP AND EXPERIMENT WITH GEOREDUNDANT TESTING FRAMEWORK (TF2GRACE)
- A CRITICAL APPRAISAL OF HUMAN RESOURCE ACCOUNTING MODELS
- Plan of Action Development: How the Universal Retailers Reconstruct their Center Business Statistics in another Host Nation
Last modified: 2018-09-15 15:46:36