Ryosuke Yamanishi
College of Information Science and Engineering, Ritsumeikan University, Japan
Risako Kagita
College of Information Science and Engineering, Ritsumeikan University, Japan
Yoko Nishihara
College of Information Science and Engineering, Ritsumeikan University, Japan
Junichi Fukumoto
College of Information Science and Engineering, Ritsumeikan University, Japan
Ladda ner artikel
Ingår i: KEER2014. Proceedings of the 5th Kanesi Engineering and Emotion Research; International Conference; Linköping; Sweden; June 11-13
Linköping Electronic Conference Proceedings 100:121, s. 1453-1463
Publicerad: 2014-06-11
ISBN: 978-91-7519-276-5
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
This paper describes a method for extracting attractive phrases of lyric focusing on linguistic expressions. Not only chorus but also linguistic expressions seem to be a cause of attractive phrases. We conducted impressive evaluation experiments to clarify the important factors of attraction of phrase. As the result; it was confirmed that “uniqueness of co-occurred terms” and “repetition” especially influenced attraction. Therefore; we modeled the uniqueness of co-occurred terms and repetition as seven mathematical features. And the proposed method detected attractive phrases using support vector machine with the modeled features; which is known as a high performance pattern recognition method. Through the attractive phrase detection experiments; we confirmed availability of the proposed method: the accuracy level and the precision was each 69% and 86%; respectively. Moreover; we discussed about the correctly detected attractive phrases comparing key sentences detected by the existing summarization methods. As the result of the discussions; the proposed method correctly detected the phrases that were ranked in low by the conventional methods though human evaluated the phrases as attractive. From these facts; it was suggested that lyrical linguistic expressions were well modeled in the proposed method; and the proposed method detected the attractive phrases better than the existing summarization method.
Abe; C. & A. Ito. (2012). A Japanese Lyrics Writing Support System for Amateur Songwriter. In Proceedings
of Signal & Information Processing Association Annual Summit and Conference (1-4). Hollywood; CA.
Eric; N.; Dan; M.; Sumit; B.; & Christopher; R. (2009). Relationships between Lyrics and Melody in Popular
Music. In Proceedings of the 10th International Society for Music Information Retrieval Conference (471-476).
Kobe; Japan.
Goto; M. (2006). A Chorus-Section Detection Method for Musical Audio Signals and Its Application to a Music
Listening Station. IEEE Transactions on Audio; Speech and Language Processing; 14(5); 1783-1794.
Joachims; T. (2008). SVM-Light; from http://svmlight.joachims.org/.
Kudo; T.; Yamamoto; K.; & Matsumoto; Y. (2004). Applying conditional random fields to Japanese morphological analysis. In Proceedings of Empirical Methods in Natural Language Processing (230-237).
Barcelona; Spain.
Lu; L.; Liu; D.; & Zhang; H. J. (2006). Automatic Mood Detection and Tracking of Music Audio Signals. IEEE Trance on Audio; Speech; and Language Processing; 14(1); 5-18.
Mayer; R. & Rauber; A. (2011). Musical Genre Classification by Ensembles of Audio and Lyrics Features. In Proceedings of the 12th International Society for Music Information Retrieval Conference (675-680). Miami;
Florida (USA).
Nichigai Associates. Incorporated. (2004) CD-Mainichi newspaper database ver. 04.
http://www.nichigai.co.jp/sales/corpus.html.
Nishihara; Y. & Sunayama; W. (2011). Text Visualization using Light and Shadow based on Topic Relevance. International Journal of Intelligent Information Processing; 2(2); 1-8.
Sunayama; W. & Yachida; M. (2005). Panoramic View System for Extracting Key Sentences Based on Viewpoints and an Application to a Search Engine. Journal of Network and Computer Applications; 28(2); 115
-127.
Ueda; T. (2010). [Well understandable lecture book for writing lyric]. YAMAHA MUSIC MEDIA CORPORATION; Tokyo. ISBN: 978-4636845082.
Wang; X.; Chen; X.; Yang; D.; & Wu; Y. (2011). Music Emotion Classification of Chinese Songs Based on Lyrics Using TF*IDF and Rhyme. In Proceedings of the 12th International Society for Musical Information
Retrieval Conference (765-770). Miami; Florida (USA).
Yamanishi; R.; Ito; Y.; & Kato; S. (2011). Relationships Between Emotional Evaluation of Music and Acoustic Fluctuation Properties. In Proceedings of 2011 IEEE Symposium on Computers & Informatics (pp. 721-726). Kuala Lumpur; Malaysia.
Zaanen; M. & Kanters; P. (2010). Automatic Mood Classification Using TF*IDF Based on Lyrics. In Proceedings of the 11th International Society for Music Information Retrieval Conference (75-80). Utrecht;
Netherlands.
