Title :
An empirical model for clustering and classification of instrumental music using machine learning technique
Author :
Hemalatha, M. ; Sasirekha, N. ; Easwari, S. ; Nagasaranya, N.
Author_Institution :
Dept. of Software Syst., Karpagam Univerisy, Coimbatore, India
Abstract :
To extract implicit knowledge and data relationships from the audio and audio similarity measure, this paper uses the audio mining techniques. A model for audio clustering and classification technique is proposed. Neural networks are used for classifying the data. The working prototype of the Music classification system has been developed and tested in MATLAB 6.5 using the signal Processing Toolbox under Windows XP operating system in a normal Pentium range of desktop computer.
Keywords :
audio signal processing; data mining; learning (artificial intelligence); multimedia computing; music; neural nets; pattern classification; pattern clustering; MATLAB 6.5; Windows XP operating system; audio classification technique; audio clustering technique; audio mining techniques; data classification; desktop computer; implicit knowledge extraction; instrumental music classification; instrumental music clustering; machine learning technique; multimedia mining; neural networks; signal processing toolbox; Artificial neural networks; Data mining; Feature extraction; Fluctuations; Mathematical model; Multimedia communication; Training; Audio mining; Classification; Clustering; Machine Learning; Neural Networks;
Conference_Titel :
Computational Intelligence and Computing Research (ICCIC), 2010 IEEE International Conference on
Conference_Location :
Coimbatore
Print_ISBN :
978-1-4244-5965-0
Electronic_ISBN :
978-1-4244-5967-4
DOI :
10.1109/ICCIC.2010.5705726
