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@article{155461,
        author = {Pavan P and Manoj K S and Shivakumar K R and Shruthi P},
        title = {Music Genre Classification with Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {1},
        pages = {922-926},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155461},
        abstract = {Tune performs a significant position in absolutely everyone’s life. Music genre type approach is based totally on Mel Frequency Cepstral coefficients (MFCC). The Mel-Frequency Cepstrum (MFC) encrypt the strength spectrum of an audio. MFCC are premeditated as the discrete Fourier transform (DCT) of the logarithm of the wave spectrum. Spectrogram is used to correlate the collection of all of Mel Frequency Cepstral Coefficient’s.  A spectrogram refers to the frequency information of a tune. It depicts the depth of frequencies on y axis and detailed time intervals on x axis. Darker coloration in spectrogram shows the more potent frequencies.  A genre of the song categorizes the tune based on the frequency. Okay Nearest Neighbor (KNN) set of rules is used for prediction.  The approach comprising of all capabilities, improves the accuracy for predicting the style of music.},
        keywords = {GTZAN, MFCC features, K nearest neighbor, Spectrogram, wav format.},
        month = {},
        }