GMM Based Indexing and Retrieval of Music Using MFCC and MPEG-7 Features

Audio which includes voice, music, and various kinds of environmental sounds, is an important type of media, and also a significant part of video. The digital music databases in place these days, people begin to realize the importance of effectively managing music databases relying on music content analysis. The goal of music indexing and retrieval system is to provide the user with capabilities to index and retrieve the music data in an efficient manner. For efficient music retrieval, some sort of music similarity measure is desirable. In this paper, we propose a method for indexing and retrieval of the classified music using Mel-Frequency Cepstral Coefficients (MFCC) and MPEG-7 features. Music clip extraction, feature extraction, creation of an index and retrieval of the query clip are the major issues in automatic audio indexing and retrieval. Indexing is done for all the music audio clips using Gaussian mixture model (GMM) models, based on the features extracted. For retrieval, the probability that the query feature vector belongs to each of the Gaussian is computed. The average Probability density function is computed for each of the Gaussians and the retrieval is based on the highest probability.