improving music mood classification using lyrics, audio and social tags

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REFERENCES Alm, E. C. O. (2008). Affect in Text and Speech. Doctoral Dissertation. University of Illinois at Urbana-Champaign. Argamon, S., Saric, M., & Stein, S. S. (2003). Style mining of electronic messages for multiple authorship discrimination: first results. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2003 (KDD'03). pp. 475-480. Asmus, E. (1995). The development of a multidimensional instrument for the measurement of affective responses to music. Psychology of Music, 13(1): 19-30. Aucouturier, J-J., & Pachet, F. (2004), Improving timbre similarity: How high is the sky? Journal of Negative Results in Speech and Audio Sciences, 1 (1). Retrieved from http://ayesha.lti.cs.cmu.edu/ojs/index.php/jnrsas/article/viewFile/2/2 on September 20, 2008. Aucouturier, J-J., Pachet, F., Roy, P., & Beurivé, A. (2007). Signal + Context = Better Classification. In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR’07). Sep. 2007, Vienna, Austria. Retrieved from http://ismir2007.ismir.net/proceedings/ISMIR2007_p425_aucouturier.pdf on October 1, 2008. Bainbridge, D., Cunningham, S. J., & Downie, J. S. (2003). How people describe their music information needs: a grounded theory analysis of music queries. In Proceedings of the 4th International Conference on Music Information Retrieval (ISMIR’03). Baltimore, Maryland, USA, pp. 221-222. Bischoff, K., Firan, C. S., Nejdl, W., & Paiu, R. (2009a). How do you feel about "Dancing Queen"? Deriving mood and theme annotations from user tags. In Proceedings of Joint Conference on Digital Libraries (JCDL’09), Austin, USA, pp. 285-294. Bischoff, K., Firan, C., Paiu, R., Nejdl, W., Laurier, C., & Sordo, M. (2009b). Music mood and theme classification - a hybrid approach. In Proceedings of the 10 th International Conference on Music Information Retrieval (ISMIR’09), Kobe, Japan, pp. 657-662. Borg, I., & Groenen, P. J. F. (2004). Modern Multidimensional Scaling: Theory and Applications. Springer. Bradley, M. M., & Lang, P. J. (1999). Affective Norms for English Words (ANEW): Stimuli, Instruction Manual and Affective Ratings. (Technical report C-1). Gainesville, FL. The Center for Research in Psychophysiology, University of Florida. Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2: 121–167. 117
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IMPROVING MUSIC MOOD CLASSIFICATION
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concatenation and late fusion (line
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ACKNOWLEDGMENTS I would have never
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TABLE OF CONTENTS LIST OF FIGURES .
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7.2.2 Best Hybrid Method ..........
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LIST OF TABLES Table 3.1 Mood categ
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music itself and creating the socia
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the ultimate judge. Thus ground tru
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The advantages have attracted resea
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Research Question 2: Which type(s)
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There are two popular approaches in
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1.4.1 Contributions to Methodology
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experimental datasets in music mood
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CHAPTER 2: LITERATURE REVIEW 2.1 MO
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hypothesizes that there are at leas
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2.2.3 What We Know about Music Mood
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encoding facial expressions, some o
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environment by real-life users. The
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1) Tempo: also called “rhythmic p
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previous research did, Bischoff et
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other. Stylistic features, borrowed
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2.5 SUMMARY This chapter provided a
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“evaluation” containing 492 wor
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which it is derived. For instance,
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3.3 COMPARISONS TO MUSIC PSYCHOLOGY
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In total, 20 of the 36 derived cate
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in today’s music listening enviro
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3.4 SUMMARY The identified categori
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CHAPTER 4: CLASSIFICATION EXPERIMEN
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accuracy = TP + TN TP + FN + FP + T
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Support Vector Machines (SVM), etc.
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4.3 SUMMARY This chapter described
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Table 5.1 Information of audio coll
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irrelevant parts such as HTML marku
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Starting from the dataset collected
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Table 5.3 Mood categories and song
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1) a song has been tagged with one
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5.2.2.4 Negative Samples Figure 5.1
Page 77 and 78: The patterns shown in this figure a
Page 79 and 80: CHAPTER 6: BEST LYRIC FEATURES This
Page 81 and 82: Liu (2006) identified discriminativ
Page 83 and 84: as feature values respectively. The
Page 85 and 86: 6.2.2 Linguistic Lyric Features In
Page 87 and 88: mixed with regard to its usefulness
Page 89 and 90: Table 6.2 Text stylistic features e
Page 91 and 92: 6.3 IMPLEMENTATION The Snowball ste
Page 93 and 94: shows the best combined feature set
Page 95 and 96: 6.4.3 Analysis of Text Stylistic Fe
Page 97 and 98: Figure 6.1 Distributions of “!”
Page 99 and 100: stylistic features appeared to be a
Page 101 and 102: Support Vector Machines (SVM) as it
Page 103 and 104: then compared to the feature concat
Page 105 and 106: According to the average accuracies
Page 107 and 108: systems and the audio-only system w
Page 109 and 110: Table 7.4 Accuracies of lyric and a
Page 111 and 112: 7.4.2 Top-Ranked Features Based on
Page 113 and 114: Again, these top-ranked features se
Page 115 and 116: However, some might argue that word
Page 117 and 118: Figure 8.1 shows a general trend th
Page 119 and 120: There are other interesting observa
Page 121 and 122: CHAPTER 9: CONCLUSIONS AND FUTURE R
Page 123 and 124: different strengths. Lyric-based sy
Page 125 and 126: close relationship with music mood.
Page 127: available, it is interesting to exp
Page 131 and 132: Hu, X., Sanghvi, V., Vong, B., On,
Page 133 and 134: McEnnis, D., McKay, C., Fujinaga, I
Page 135 and 136: Thayer, R. E. (1989). The Biopsycho
Page 137 and 138: 18. “music fade” at end of a se
Page 139 and 140: 48. Segment annotation plus specifi
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