earticle

영어

The digital music market has been growing significantly in the past years. In music streaming services, music recommendation plays a key role, but Korean users’ recognition about their music service is not high because the service’s recommendation accuracy is not good. Therefore, this paper suggests technique to predict the user’s musical taste. This technique proceeds through a four-step process; data collection, data pre-processing, feature extraction, and machine learning. Collection of data was taken from TOP 100 chart in ‘Melon’, the number one music service provider in Korea from December 2013 to March 2015. Then, collected MP3 file format is converted into WAV file format. In the stage of feature extraction, we classify the genre from the music’s metadata and extract factors that can be taken using STFT’s ZCR, Spectral Rolloff, Spectral Flux. In the stage of machine learning, we produce a prediction model in a variety of classification techniques. To measure the performance of the created prediction model, 456 data were used for training dataset and 130 data were used for validation dataset. Since the results of experiment show an average of 78% of accuracy, the proposed technique seems to be effective.