Article | KEER2014. Proceedings of the 5th Kanesi Engineering and Emotion Research; International Conference; Linköping; Sweden; June 11-13 | Attractive Phrase Detection from Musical Lyric Focusing on Linguistic Expressions
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Title:
Attractive Phrase Detection from Musical Lyric Focusing on Linguistic Expressions
Author:
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
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Year:
2014
Conference:
KEER2014. Proceedings of the 5th Kanesi Engineering and Emotion Research; International Conference; Linköping; Sweden; June 11-13
Issue:
100
Article no.:
121
Pages:
1453-1463
No. of pages:
11
Publication type:
Abstract and Fulltext
Published:
2014-06-11
ISBN:
978-91-7519-276-5
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press; Linköpings universitet


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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 ‚Äúuniqueness of co-occurred terms‚ÄĚ and ‚Äúrepetition‚ÄĚ 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.

Keywords: Music; Lyric; Attractive Phrase; Natural Language Processing

KEER2014. Proceedings of the 5th Kanesi Engineering and Emotion Research; International Conference; Linköping; Sweden; June 11-13

Author:
Ryosuke Yamanishi, Risako Kagita, Yoko Nishihara, Junichi Fukumoto
Title:
Attractive Phrase Detection from Musical Lyric Focusing on Linguistic Expressions
References:

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.

KEER2014. Proceedings of the 5th Kanesi Engineering and Emotion Research; International Conference; Linköping; Sweden; June 11-13

Author:
Ryosuke Yamanishi, Risako Kagita, Yoko Nishihara, Junichi Fukumoto
Title:
Attractive Phrase Detection from Musical Lyric Focusing on Linguistic Expressions
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