improving music mood classification using lyrics, audio and social tags

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Futrelle, J., & Downie, J. S. (2002). Interdisciplinary communities and research issues in music information retrieval. In Proceedings of the 3rd International Conference on Music Information Retrieval (ISMIR’02). Paris, France, pp. 215-221. Gabrielsson, A., & Lindström, E. (2001). The influence of musical structure on emotional expression. In P. N. Juslin and J. A. Sloboda (Eds.), Music and Emotion: Theory and Research. New York: Oxford University Press, pp. 223-248. Geleijnse, G.., Schedl, M., & Knees, P. (2007). The quest for ground truth in musical artist tagging in the social web era. 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_p525_geleijnse.pdf on September 20, 2008. Guy, M., & Tonkin, E. (2006). Tidying up tags. D-Lib Magazine. 12(1). Retrieved from http://www.dlib.org/dlib/january06/guy/01guy.html on November 18, 2008. He, H., Jin, J., Xiong, Y., Chen, B., Sun, W., & Zhao, L. (2008). Language feature mining for music emotion classification via supervised learning from lyrics. Advances in Computation and Intelligence, Lecture Notes in Computer Science, 5370: 426-435. Springer Berlin / Heidelberg. Hevner, K. (1936). Experimental studies of the elements of expression in music. American Journal of Psychology, 48: 246-268. Hofmann, T. (1999). Probabilistic latent semantic analysis. In Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence. Jul. 1999, Stockholm, Sweden, pp. 289-296. Hu, X., Bay, M., & Downie, J. S. (2007a). Creating a simplified music mood classification groundtruth set, In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR’07). Sep. 2007, Vienna, Austria, pp. 309-310. Hu, X., & Downie, J. S. (2007). Exploring mood metadata: relationships with genre, artist and usage metadata, In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR’07). Sep. 2007, Vienna, Austria, pp. 462-467. Hu, X. Downie, J. S., & Ehmann, A. (2007b) Distinguishing editorial and customer critiques of cultural objects using text mining, In Proceedings of the joint conference of the Association for Literary and Linguistic Computing and the Association for Computers and the Humanities (ACH/ALLC), Jun. 2007, Champaign, IL. Hu, X., Downie, J. S., & Ehmann, A. (2009a). Lyric text mining in music mood classification, In Proceedings of the 10th International Conference on Music Information Retrieval (ISMIR’09), Kobe, Japan, pp. 411 – 416. Hu, X., Downie, J. S., Laurier, C., Bay, M., & Ehmann, A. (2008a). The 2007 MIREX Audio Music Classification task: lessons learned, In Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR’08). Sep. 2008, Philadelphia, USA, pp. 462-467. 119
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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
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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 and 128: available, it is interesting to exp
Page 129: Byrd, D., & Crawford, T. (2002). Pr
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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