Large-Scale Cover Song Retrieval System Developed Using Machine Learning Approaches

摘要

Large-scale cover song retrieval systems should be able to calculate song-to-song similarity and accommodate differences in timing, key, and tempo. Simple vector distance measure is not adequately powerful to perform cover song recognition, and expensive solutions such as dynamic time warping do not scale to millions of instances, making cover song retrieval inappropriate for commercial-scale applications. In this work, we used the content-based music features of songs as input and transformed them into vectors by using the 2D Fourier transform approach. Furthermore, we explored different machine learning approaches to reinforce the pattern of these vectors. By projecting the songs into a semantic vector space, we can use the efficient nearest neighbor algorithm to compare the similarity of songs and retrieve the most similar songs from the large-scale database. The proposed system is not only efficient enough to perform scalable content-based music retrieval but can also develop the potential of machine learning approaches, making similar music recognition applications faster and more accurate.