Audio 120 kbps Classification Based on Feature Clustering Algorithm
Jay Prakash, K.M.V Ramana, Mr. R. Vamsidhar Raju · International Journal of Modern Trends in Engineering and Research · 2015
Feature clustering is a powerful method to reduce the dimensionality of feature vectors for audio 120 kbps classifications. In this paper, we propose a fuzzy similarity-based self- constructing algorithm for feature clustering. The sounds in the feature vector of an audio set a regrouped into clusters, based on 120 kbps similarity test. Sounds that are similar to each other are grouped into the same cluster. Set tempo to the sound 120 kbps for more accuracy results and reliability .When all the sounds have been fed in, a desired Number of clusters are formed automatically. We then have one extracted feature for each cluster. The extracted feature, corresponding to a cluster is a weighted combination of the sounds contained in the cluster. By this algorithm, the derived membership Functions match closely with and describe properly the real distribution of the training data. Besides, the user need not specify the number of extracted features in advance and trial-and-error for determining the appropriate number of extracted feature scan then is avoided. Index Terms—feature clustering, feature reduction, audio classifications.