• R. Thiruvengatanadhan

Automatic music classification is a fundamental problem for music indexing, content-based music retrieval, music recommendation and online music distribution. Automatic audio classification is very useful in audio indexing; content based audio retrieval and online audio distribution. The accuracy of the classification relies on the strength of the features and classification scheme. In this work a speech/music discrimination system is developed which utilizes the Zero Crossing Rate (ZCR), Short Time Energy (STE), Spectral Centroid and Spectral Flux as the acoustic feature. This paper analyses neural networks and their precision when they both stumble upon same targets in similar category. The analysis is done on radial basis function neural network (RBFNN) then a conclusion is formed on the basis of their performance and efficiency.

Feature Extraction, Pattern Classification, Zero Crossing Rate (ZCR), Short Time Energy (STE), Spectral Centroid, Spectral Flux, Radial Basis Function Neural Network.

"Speech/Music Classification using Spectral features and RBFNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 1, page no.700-703, January-2019, Available :http://www.jetir.org/papers/JETIR1901396.pdf

"Speech/Music Classification using Spectral features and RBFNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 1, page no. pp700-703, January-2019, Available at : http://www.jetir.org/papers/JETIR1901396.pdf