Detection of similarity in music files using signal level analysis

Abstract

In today's age of digital media, the collection of music files available to the general public is extremely diverse. As with any such set of data, efforts must be made to classify and categorize these files in order to facilitate easy access and searching. Songs can be classified based on attributes available in the music file's metadata such as artist, album, year of release, length, etc. However, if the similarity between two songs is to be determined, a simple comparison of metadata is not only unsatisfactory, the metadata itself might not be available. Therefore, a method of comparison independent of the availability of metadata is required. In this work, a comparison method has been proposed involving the use of musical parameters such as tempo, key and signal envelope, which are extracted from the music file through signal level analysis. Genre is also computed using a support vector machine (SVM) classifier and used to estimate the similarity between two songs. � 2016 IEEE.