An integrated music recommendation system

In this paper, an integrated music recommendation system is proposed, which contains the functions of automatic music genre classification, automatic music emotion classification, and music similarity query. A novel tempo feature, named as log-scale modulation frequency coefficients, is presented in this paper. With AdaBoost algorithm, the proposed tempo feature is combined with timbre features and improves the performance of music genre and emotion classification. Comparing with the conventional methods based on timbre features, the precision of five-genre classification is enhanced from 86.8% to 92.2% and the accuracy of four-emotion classification is increased from 86.0% to 90.5%. Based on the results of music genre/emotion classification, we design a similarity query scheme, which can speed up the similarity query process without decreasing the precision. Furthermore, all the features employed in this paper are extracted from the data of MP3 partially decoding, which significantly reduces the feature extraction time