improving music mood classification using lyrics, audio and social tags

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APPENDIX B. PAPERS AND POSTERS ON THIS RESEARCH* Hu, X. (2009). Categorizing Music Mood in Social Context. In Proceedings of the Annual Meeting of ASIS&T (CD-ROM), Nov. 2009, Vancouver, Canada. Hu, X. (2009). Combining Text and Audio for Music Mood Classification in Music Digital Libraries. IEEE Bulletin of Technical Committee on Digital Libraries (TCDL), Vol. 5. No. 3. Accessible at http://www.ieee-tcdl.org/Bulletin/v5n3/Hu/hu.html. Hu, X. (2010). Multi-modal Music Mood Classification. Presented in the Jean Tague-Sutcliffe Doctoral Research Poster session at the ALISE Annual Conference, Jan. 2010, Boston, MA. (3 rd Place Award). Hu, X. (2010). Music and Mood: Where Theory and Reality Meet. In Proceedings of the 5 th iConference, University of Illinois at Urbana-Champaign, Feb. 2010, Champaign, IL (Best Student Paper Award). Hu, X., & Downie, J. S. (2010). Improving Mood Classification in Music Digital Libraries by Combining Lyrics and Audio. In Proceedings of the Joint Conference on Digital Libraries’2010, (JCDL), Jun. 2010, Surfers Paradise, Australia, pp.159-168. (Best Student Paper Award). Hu, X., & Downie, J. S. (2010). When Lyrics Outperform Audio for Music Mood Classification: A Feature Analysis. In Proceedings of the 11th International Conference on Music Information Retrieval (ISMIR), Aug. 2010, Utrecht, Netherlands, pp.619-624. * The author of this dissertation holds the copyright to all of these publications. 129
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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
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CHAPTER 6: BEST LYRIC FEATURES This
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Liu (2006) identified discriminativ
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as feature values respectively. The
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6.2.2 Linguistic Lyric Features In
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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
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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 and 130: Byrd, D., & Crawford, T. (2002). Pr
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: 48. Segment annotation plus specifi
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