RESEARCH OF MUSIC CLASSIFICATION BASED ON MOOD RECOGNITION

Dhruja Patel , Khushboo Trivedi

Abstract


Music emotion is a vital component in the field of multimedia database recovery and computational   musicology. The online musical datasets  are major challenges for  searching, retrieving, and  organizing the music content. Therefore, there is  a require for robust automatic music emotion classifier system for  organizing variety music pieces into different classes according to the specific viable information. Basic components are to be considered  for music emotion classification audio feature origin and  classifier design. In user propose diverse audio  features to precisely characterize the music substance. The feature sets belong to  groups dynamic, rhythmic, spectral, and harmonic. Four statistical parameters are considered as representatives, including  the fourth-order central moments of each feature as  well as covariance part. Number of unimportant parameters is forced by minimum unemployment maximum relevance(MRMR)algorithm and   principal component analysis(PCA). Support Vector  Machine(SVM) is used as a classifiers  to classify the music mood recognition.

Keywords


Music emotion recognition, Feature extraction, Two level classification, Music mood classification.

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References


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