The week's pick
Random Articles
Reseach Article
Music Generation and Song Popularity Prediction using Artificial Intelligence - An Overview
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 182 - Number 50 |
| Year of Publication: 2019 |
| Authors: Vaishali Jabade, Vedang Deshpande, K. Aditya Kumar |
10.5120/ijca2019918762
|
Vaishali Jabade, Vedang Deshpande, K. Aditya Kumar . Music Generation and Song Popularity Prediction using Artificial Intelligence - An Overview. International Journal of Computer Applications. 182, 50 ( Apr 2019), 33-39. DOI=10.5120/ijca2019918762
Abstract
As the musical industry is rapidly growing, there is an increasing demand for digital platforms for production and consumption of music. With this digitization, a lot of data regarding artists and tracks is available for analysis. Since music production is also digitized, methods for automating this process are emerging as well. The goal of this paper is to explore the methods of generation and popularity prediction. This will benefit , both the creators(music producers, music directors, arrangers, sound engineers) and also the business personnel (Artists and Repertoire, Record labels, artist managers, music distributors and streaming services). Music generation is the process of composing, and arranging melodies(composed of musical notes, within the restrictions of music theory). The popularity of a song depends on various factors such as hotness of the artist, tempo, scale, melody, emotion etc.
References
- Motoki Kikuchi and Yuko Osana, “Automatic Melody Generation considering Chord Progression by Genetic Algorithm”, 2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC)
- Junghyuk Lee and Jong-Seok Lee, “Music Popularity: Metrics, Characteristics, and Audio-based Prediction”, Journal of LATEX Class Files, VOL. 14, NO. 8, August 2015
- S. Traverso, M. Ahmed, M. Garetto, P. Giaccone, E. Leonardi, and S. Niccolini, “Unravelling the impact of temporal and geographical locality in content caching systems,” IEEE Transactions on Multimedia, vol. 17, no. 10, pp. 1839–1854, Oct. 2015.
- B. Logan, “Music recommendation from song sets.” in Proceedings of International Society for Music Information Retrieval Conference, 2004, pp. 425–428.
- J.-J. Aucouturier and F. Pachet, “Scaling up music playlist generation,” in Proceedings of the IEEE International Conference on Multimedia and Expo, vol. 1, 2002, pp. 105–108
- J. P. Friedlander, “News and notes on 2013 RIAA music industry shipment and revenue statistics,” Recording Industry Association of America, Tech. Rep., 2014
- J. Salamon, E. Gomez, D. P. W. Ellis, and G. Richard, “Melody Extraction from Polyphonic Music Signals: Approaches, applications, and challenges,” IEEE Signal Process. Mag., vol. 31, no. 2, pp. 118–134, Mar. 2014.
- C. D. Manning, H. Schutze: Foundations of Statistical Natural Language Processing, MIT Press, 1999
- M.G. Viraj Lakshitha and K.L. Jayaratne, “Melody Analysis for Prediction of the Emotions Conveyed by Sinhala Songs”, 2016 IEEE International Conference on Information and Automation for Sustainability (ICIAfS)
- M. Takano and Y. Osana : "Automatic composition system using genetic algorithm and N-gram model considering melody blocks," Proceedings of IEEE Congress on Evolutionary Computation, Brisbane, 2012.
- D. E. Goldberg: Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Longman Publishing, 1989
- B. Han, S. Ho, R. Dannenberg, and E. Hwang, “Smers: Music emotion recognition using support vector regression,” 2009
- M. Schoen and E. Gatewood, “The mood effects of music,” Eff.Music, 1927.
- J. Salamon and E. Gómez, “Melody extraction from polyphonic music signals using pitch contour characteristics,” Audio, Speech, Lang. …, 2012.
- X. Hu, “Music and mood: Where theory and reality meet,” 2010.
- R. Paiva, “An algorithm for melody detection in polyphonic recordings,” Proc. 2nd Music Inf. Retr. …, 2005.
- J. Salamon, B. Rocha, and E. Gómez, “Musical genre classification using melody features extracted from polyphonic music signals,” Acoust. Speech Signal …, 2012
- G. Poliner and D. Ellis, “Melody transcription from music audio: Approaches and evaluation,” Audio, Speech, 2007
- B. Rocha, R. Panda, and R. Paiva, “Music Emotion Recognition: The Importance of Melodic Features,” 5th Int. Work., 2013.
- Thomas Birtchnell, “Listening without ears: Artificial intelligence in audio mastering”, Big Data & Society. July–December 2018: 1–16
- S. D. Roy, T. Mei, W. Zeng, and S. Li, “Towards cross-domain learning for social video popularity prediction,” IEEE Transactions on Multimedia, vol. 15, no. 6, pp. 1255–1267, Oct. 2013.
Index Terms
Keywords