Song Similarity Classication

Abstract: The purpose of this study was to investigate the possibility of automatically classifying the similarity of song pairs. The machine learning algorithm K-Nearest Neighbours , combined with both bootstrap aggregating and an attribute selection classier, was rst trained by combining the acoustic features of 45 song pairs extracted from the Million Song Dataset with usersubmitted similarity for each pair. The trained algorithm was then utilized to predict the similarity between 50 hand-picked and about 4000 randomly chosen pop and rock songs from the Million Song Dataset. Finally, the algorithm was subjectively evaluated by asking users to identify which out of two randomly ordered songs, one with a low and one with a high predicted similarity, they found most similar to a target song. The users picked the same song as the algorithm 365 out of 514 times, giving the algorithm an accuracy of 71%. The results indicates that automatic and accurate classication of song similarity may be possible and thus may be used in music applications. Further research on improving the current algorithm or nding alternative algorithms is warranted to draw further conclusions about the viability of using automatically classied song similarity in real-world applications.