improving music mood classification using lyrics, audio and social tags

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To Mom, Huamin, and Ethan iv
ACKNOWLEDGMENTS I would have never made it to this stage of Ph.D. study without the help and support from many people. First and foremost, I am greatly in debt to my academic advisor and mentor, Dr. Linda C. Smith. I am blessed with an advisor and mentor who is supportive and encouraging, sincerely cares about students professionally and personally, provides detailed guidance while at the same time respecting one’s initiatives and academic creativity, and treats her advisees as future fellow scholars. The impact which Linda has had on me will keep motivating and guiding me as I step beyond my dissertation. I also cannot say thank you enough to my research director, Dr. J. Stephen Downie. I am grateful that Stephen introduced me to the general topic of Music Information Retrieval and then gave me the freedom to identify a project which fit my interests. He also set me up with research funding and the opportunity to gain hands-on experience on real-world research projects. In addition, Stephen offered incredible guidance on writing research articles as well as completing this manuscript of my dissertation. Through interactions with Stephen, I also learned the benefits of efficiency and the active pursuit of research opportunities. Many thanks go to Dr. ChengXiang Zhai. An early experience working with Cheng opened my eyes to information retrieval and text analytics. Besides helping me make progress on research by giving me invaluable feedback, advice and encouragement, Cheng also inspires me to mature as an academic and consider the big picture of research in information science. Moreover, I am also very grateful to Dr. P. Bryan Heidorn. Bryan was my research supervisor when I first joined the Ph.D. program and has since served on every committee of v
Page 1 and 2: IMPROVING MUSIC MOOD CLASSIFICATION
Page 3: concatenation and late fusion (line
Page 7 and 8: TABLE OF CONTENTS LIST OF FIGURES .
Page 9 and 10: 7.2.2 Best Hybrid Method ..........
Page 11 and 12: LIST OF TABLES Table 3.1 Mood categ
Page 13 and 14: music itself and creating the socia
Page 15 and 16: the ultimate judge. Thus ground tru
Page 17 and 18: The advantages have attracted resea
Page 19 and 20: Research Question 2: Which type(s)
Page 21 and 22: There are two popular approaches in
Page 23 and 24: 1.4.1 Contributions to Methodology
Page 25 and 26: experimental datasets in music mood
Page 27 and 28: CHAPTER 2: LITERATURE REVIEW 2.1 MO
Page 29 and 30: hypothesizes that there are at leas
Page 31 and 32: 2.2.3 What We Know about Music Mood
Page 33 and 34: encoding facial expressions, some o
Page 35 and 36: environment by real-life users. The
Page 37 and 38: 1) Tempo: also called “rhythmic p
Page 39 and 40: previous research did, Bischoff et
Page 41 and 42: other. Stylistic features, borrowed
Page 43 and 44: 2.5 SUMMARY This chapter provided a
Page 45 and 46: “evaluation” containing 492 wor
Page 47 and 48: which it is derived. For instance,
Page 49 and 50: 3.3 COMPARISONS TO MUSIC PSYCHOLOGY
Page 51 and 52: In total, 20 of the 36 derived cate
Page 53 and 54: in today’s music listening enviro
Page 55 and 56:
3.4 SUMMARY The identified categori
Page 57 and 58:
CHAPTER 4: CLASSIFICATION EXPERIMEN
Page 59 and 60:
accuracy = TP + TN TP + FN + FP + T
Page 61 and 62:
Support Vector Machines (SVM), etc.
Page 63 and 64:
4.3 SUMMARY This chapter described
Page 65 and 66:
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
Page 81 and 82:
Liu (2006) identified discriminativ
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as feature values respectively. The
Page 85 and 86:
6.2.2 Linguistic Lyric Features In
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mixed with regard to its usefulness
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Table 6.2 Text stylistic features e
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6.3 IMPLEMENTATION The Snowball ste
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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 “!”
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stylistic features appeared to be a
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Support Vector Machines (SVM) as it
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then compared to the feature concat
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According to the average accuracies
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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
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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 and 140:
48. Segment annotation plus specifi
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