TY - GEN
T1 - Detection of similarity in music files using signal level analysis
AU - Thomas, Mathew
AU - Jothish, Mintu
AU - Thomas, Navin
AU - Koolagudi, Shashidhar G.
AU - Srinivasa Murthy, Y. V.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/8
Y1 - 2017/2/8
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85015358285
UR - https://www.scopus.com/inward/citedby.url?scp=85015358285&partnerID=8YFLogxK
U2 - 10.1109/TENCON.2016.7848297
DO - 10.1109/TENCON.2016.7848297
M3 - Conference contribution
AN - SCOPUS:85015358285
T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON
SP - 1650
EP - 1654
BT - Proceedings of the 2016 IEEE Region 10 Conference, TENCON 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Region 10 Conference, TENCON 2016
Y2 - 22 November 2016 through 25 November 2016
ER -