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@article{151838,
        author = {UTKARSH UPADHYAY and Sonu Kumar and Prateek Dubey and Aman Singh and Miss Geetanjali},
        title = {Music Genre Classification},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {1},
        pages = {725-728},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=151838},
        abstract = {Classification of music genre has always been an interest in the area of music and musical data. Classification of genre can be very important to explain some interesting problems such as creating song references, exploring related songs, finding groups  which will like that specific song. The aim of our project is to find the  machine learning algorithm that predicts the genre of songs using k-nearest neighbor (k-NN) and Support Vector Machine (SVM). This paper also gives the difference between k-nearest neighbor (k-NN) and Support Vector Machine (SVM) with the help of principal component analysis (PCA). The Mel Frequency Cepstral Coefficients (MFCC) is used to get the information for the data set. Also, the MFCC features are used for a particular track. From the  outcome of the project, we found that without the dimensionality reduction both k-nearest neighbor and Support Vector Machine (SVM) gave more accurate results than dimensionality reduction. Overall the Support Vector Machine (SVM) is a much more effective classifier for classification of music genres. It had an  accuracy of around 75%.},
        keywords = {K-nearest neighbor (k-NN), Support Vector Machine (SVM),music genre, Mel Frequency Cepstral Coefficients (MFCC)},
        month = {},
        }