A New System For Audio Mining: Using of MDT+ for automatic prediction of new singers success

Online music diffusion and distribution is becoming increasingly important and commercial and personal databases are increasing in a considerable way. Nowadays, it's necessary to have tools that allow classifying and reaching these bases by carrying out analyzes through musical contents. The work included in this paper consists of automatic prediction of new singers success. This work lies within the scope of audio mining including of the heterogeneous data. Work can be divided into two parts: in the first part, we treat the audio data to make a classification of the Algerian singers' voices. There exist two categories of the singing voices classification (type and quality), each category has several classes. Within this framework, we proposed to use a vector of characteristics which contains the descriptors of MPEG-7 + the descriptors Not-MPEG-7 and Standard T2 FGMMs "Type 2 Fuzzy Gaussian Mixture Models" for modeling and classification of singing voice. By using the results of the singing voices classification with the statistics of every singer, a new database has been created. The second part of work consists of the data mining to answer our starting question "are our young people's musical tastes poor". In this part, we proposed a new method of data excavation, based on the decision trees called: Multi Decision Tree (MDT+). Finally, we used the MDT+ method to excavate our database.