K. Bischoff, C. S. Firan, M. Georgescu, W. Nejdl, and R. Paiu, Social knowledgedriven music hit prediction, International Conference on Advanced Data Mining and Applications, pp.43-54, 2009.
DOI : 10.1007/978-3-642-03348-3_8

J. A. Burgoyne, D. Bountouridis, J. V. Balen, and H. Honing, Hooked: A game for discovering what makes music catchy, International Society for Music Information Retrieval Conference, pp.245-250, 2013.

S. H. Chon, M. Slaney, and J. Berger, Predicting success from music sales data, Proceedings of the 1st ACM workshop on Audio and music computing multimedia , AMCMM '06, pp.83-88, 2006.
DOI : 10.1145/1178723.1178736

R. Dhanaraj and B. Logan, Automatic prediction of hit songs, International Society for Music Information Retrieval Conference, pp.488-491, 2005.

A. Gkiokas, V. Katsouros, G. Carayannis, and T. Stajylakis, Music tempo estimation and beat tracking by applying source separation and metrical relations, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.421-424, 2012.
DOI : 10.1109/ICASSP.2012.6287906

D. Karydi, I. Karydis, and I. Deliyannis, Legal issues in using musical content from itunes and youtube for music information retrieval, International Conference on Information Law, 2012.

Y. Kim, B. Suh, and K. Lee, #nowplaying the future billboard, Proceedings of the first international workshop on Social media retrieval and analysis, SoMeRA '14, pp.51-56, 2014.
DOI : 10.1145/2632188.2632206

N. Koenigstein, Y. Shavitt, and N. Zilberman, Predicting Billboard Success Using Data-Mining in P2P Networks, 2009 11th IEEE International Symposium on Multimedia, pp.466-470, 2009.
DOI : 10.1109/ISM.2009.73

D. Makris, K. Kermanidis, and I. Karydis, The Greek Audio Dataset, IFIP Advances in Information and Communication Technology, pp.165-173, 2014.
DOI : 10.1007/978-3-662-44722-2_18
URL : https://hal.archives-ouvertes.fr/hal-01391043

D. Mcennis, C. Mckay, I. Fujinaga, and P. Depalle, jaudio: A feature extraction library, International Society for Music Information Retrieval Conference, pp.600-603, 2005.

Y. Ni, R. Santos-rodriguez, M. Mcvicar, and T. De-bie, Hit song science once again a science? In: International Workshop on Machine Learning and Music, 2011.

F. Pachet, Hit Song Science, Music Data Mining, pp.305-326, 2011.
DOI : 10.1201/b11041-15

F. Pachet and P. Roy, Hit song science is not yet a science, International Society for Music Information Retrieval Conference, pp.355-360, 2008.
DOI : 10.1201/b11041-15

A. Singhi and D. G. Brown, Hit song detection using lyric features alone, International Society for Music Information Retrieval Conference, 2014.