improving music mood classification using lyrics, audio and social tags

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mine during my Ph.D. study. He provided kind support and guidance as I familiarized myself with this new academic and living environment. Over the years, Bryan has given me plenty of advice, not only on research methodology, but also on future career paths. I am thankful to the Graduate School of Library and Information Science for awarding me a Dissertation Completion Fellowship and providing me with financial aid to complete this program and to attend academic conferences, so that I could meet and communicate with researchers from other parts of the world. I also wanted to thank my teammates in the International Music Information Retrieval Systems Evaluation Laboratory (IMIRSEL): Mert Bay, Andreas Ehmann, Anatoliy Gruzd, Cameron Jones, Jin Ha Lee and Kris West, for their wonderful collaboration and beneficial discussions on research as well as the sweet memories of marching to conferences together. I also thank my peer doctoral students who shared my joy and frustrations during the years in the program. It was a privilege to grow up with them all. Finally, I simply owe too much to my family, especially my mom, my husband and my son. They endured the long process with great patience and always believe in and have confidence in me. This dissertation is dedicated to them. vi
TABLE OF CONTENTS LIST OF FIGURES ............................................................................................................... x LIST OF TABLES ................................................................................................................ xi CHAPTER 1: INTRODUCTION ......................................................................................... 1 1.1 INTRODUCTION ......................................................................................................... 1 1.2 ISSUES IN MUSIC MOOD CLASSIFICATION .......................................................... 3 1.2.1 Mood Categories ..................................................................................................... 3 1.2.2 Ground Truth Datasets ............................................................................................ 3 1.2.3 Multi-modal Classification ..................................................................................... 4 1.3 RESEARCH QUESTIONS ............................................................................................ 5 1.3.1 Identifying Mood Categories from Social Tags ...................................................... 5 1.3.2 Best Lyric Features ................................................................................................. 7 1.3.3 Lyrics vs. Audio ...................................................................................................... 8 1.3.4 Combining Lyrics and Audio .................................................................................. 9 1.3.5 Learning Curves and Audio Lengths .................................................................... 10 1.3.6 Research Question Summary ................................................................................ 11 1.4 CONTRIBUTIONS ..................................................................................................... 11 1.4.1 Contributions to Methodology .............................................................................. 12 1.4.2 Contributions to Evaluation .................................................................................. 13 1.4.3 Contributions to Application ................................................................................. 14 1.5 SUMMARY ................................................................................................................. 15 CHAPTER 2: LITERATURE REVIEW........................................................................... 16 2.1 MOOD AS A NOVEL METADATA TYPE FOR MUSIC ......................................... 16 2.2 MOOD IN MUSIC PSYCHOLOGY ........................................................................... 16 2.2.1 Mood vs. Emotion ................................................................................................. 17 2.2.2 Sources of Music Mood ........................................................................................ 19 2.2.3 What We Know about Music Mood ..................................................................... 20 2.2.4 Music Mood Categories ........................................................................................ 21 2.3 MUSIC MOOD CLASSIFICATION ........................................................................... 24 2.3.1 Audio-based Music Mood Classification .............................................................. 24 2.3.2 Text-based Music Mood Classification ................................................................ 26 2.3.3 Music Mood Classification Combining Audio and Text ...................................... 27 2.4 LYRICS AND SOCIAL TAGS IN MUSIC INFORMATION RETRIEVAL ............. 28 2.4.1 Lyrics .................................................................................................................... 29 2.4.2 Social Tags ............................................................................................................ 31 2.5 SUMMARY ................................................................................................................. 32 CHAPTER 3: MOOD CATEGORIES IN SOCIAL TAGS ............................................ 33 3.1 IDENTIFYING MOOD CATEGORIES FROM SOCIAL TAGS ............................... 33 3.1.1 Identifying Mood-related Terms ........................................................................... 33 3.1.2 Obtaining Mood-related Social Tags .................................................................... 34 vii
Page 1 and 2: IMPROVING MUSIC MOOD CLASSIFICATION
Page 3 and 4: concatenation and late fusion (line
Page 5: ACKNOWLEDGMENTS I would have never
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
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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
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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
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6.4.3 Analysis of Text Stylistic Fe
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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
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Table 7.4 Accuracies of lyric and a
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7.4.2 Top-Ranked Features Based on
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Again, these top-ranked features se
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However, some might argue that word
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Figure 8.1 shows a general trend th
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There are other interesting observa
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CHAPTER 9: CONCLUSIONS AND FUTURE R
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different strengths. Lyric-based sy
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close relationship with music mood.
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available, it is interesting to exp
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Byrd, D., & Crawford, T. (2002). Pr
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Hu, X., Sanghvi, V., Vong, B., On,
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McEnnis, D., McKay, C., Fujinaga, I
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Thayer, R. E. (1989). The Biopsycho
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18. “music fade” at end of a se
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48. Segment annotation plus specifi
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