CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
Reseach Article

Mood based Playlist Generation for Hindi Popular Music: A Proposed Model

by Kunjal Gajjar, Siddhi Shah
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 127 - Number 14
Year of Publication: 2015
Authors: Kunjal Gajjar, Siddhi Shah
10.5120/ijca2015906505

Kunjal Gajjar, Siddhi Shah . Mood based Playlist Generation for Hindi Popular Music: A Proposed Model. International Journal of Computer Applications. 127, 14 ( October 2015), 11-14. DOI=10.5120/ijca2015906505

@article{ 10.5120/ijca2015906505,
author = { Kunjal Gajjar, Siddhi Shah },
title = { Mood based Playlist Generation for Hindi Popular Music: A Proposed Model },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 127 },
number = { 14 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 11-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume127/number14/22796-2015906505/ },
doi = { 10.5120/ijca2015906505 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:19:53.542673+05:30
%A Kunjal Gajjar
%A Siddhi Shah
%T Mood based Playlist Generation for Hindi Popular Music: A Proposed Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 127
%N 14
%P 11-14
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Large digital databases of Hindi music are available which creates an opportunity of filtering this data with multiple parameters. One of the most important parameter used by the listeners are their moods. This paper focuses on Automatic generation of mood based playlist for Hindi popular music with minimum user intervention. There are two major modules of the proposed system. The first module identifies user’s mood based on the inputs from social media and messaging app like WhatsApp. The second module is responsible for tagging the songs of available database. Tagging is done on the basis of Genre, Artists, Tempo and Lyrics. Using the above mentioned modules, mood based playlist can be generated for user.

References
  1. Pohle, Tim, Elias Pampalk, and Gerhard Widmer. "Generating similarity-based playlists using traveling salesman algorithms." Proceedings of the 8th International Conference on Digital Audio Effects (DAFx-05). 2005.
  2. Van Zaanen, Menno, and Pieter Kanters. "Automatic Mood Classification Using TF* IDF Based on Lyrics." ISMIR. 2010.
  3. Goodman, Ron, and Howard N. Egan. "Automatic hierarchical categorization of music by metadata." U.S. Patent No. 6,928,433. 9 Aug. 2005.
  4. Brilis, Spyros, et al. "Mood classification using lyrics and audio: a case-study in Greek Music." Artificial Intelligence Applications and Innovations. Springer Berlin Heidelberg, 2012.
  5. Logan, Beth. "Content-Based Playlist Generation: Exploratory Experiments."ISMIR. 2002.
  6. Cunningham, Sally Jo, David Bainbridge, and Annette Falconer. "" More of an art than a science": Supporting the creation of playlists and mixes." (2006).
  7. Goto, Masataka, and Takayuki Goto. "Musicream: New Music Playback Interface for Streaming, Sticking, Sorting, and Recalling Musical Pieces."ISMIR. 2005.
  8. Liu, Dan, Lie Lu, and HongJiang Zhang. "Automatic mood detection from acoustic music data." ISMIR. 2003.
  9. Cardoso, Luís, Renato Panda, and Rui Pedro Paiva. "MOODetector: A prototype software tool for mood-based playlist generation." Simposio de Informatica–INForum 2011. Vol. 124. 2011.
  10. Davies, Matthew EP, and Mark D. Plumbley. "Context-dependent beat tracking of musical audio." Audio, Speech, and Language Processing, IEEE Transactions on 15.3 (2007): 1009-1020.
  11. Ujlambkar, Aniruddha M., and Vahida Z. Attar. "Automatic Mood Classification Model for Indian Popular Music." Modelling Symposium (AMS), 2012 Sixth Asia. IEEE, 2012.
  12. McKay, Cory, Ichiro Fujinaga, and Philippe Depalle. "jAudio: A feature extraction library." Proceedings of the International Conference on Music Information Retrieval. 2005.
  13. Davies, Matthew EP, and Mark D. Plumbley. "Context-dependent beat tracking of musical audio." Audio, Speech, and Language Processing, IEEE Transactions on 15.3 (2007): 1009-1020.
  14. Ujlambkar, Amey, et al. "Mood Based Music Categorization System for Bollywood Music." International Journal of Advanced Computer Research 4.1 (2014): 223.
  15. Yang, Dan, and Won-Sook Lee. "Music emotion identification from lyrics."Multimedia, 2009. ISM'09. 11th IEEE International Symposium on. IEEE, 2009.
  16. Furuya, Mizuki, Hung-Hsuan HUANG, and Kyoji Kawagoe. "Evaluation of Music Classification Method based on Lyrics of English Songs." Proceedings of the International MultiConference of Engineers and Computer Scientists. Vol. 1. 2015.
  17. Hu, Xiao, and J. Stephen Downie. "When Lyrics Outperform Audio for Music Mood Classification: A Feature Analysis." ISMIR. 2010.
Index Terms

Computer Science
Information Sciences

Keywords

Music Information Retrieval Mood Classification genre lyrics tempo analysis social media