improving music mood classification using lyrics, audio and social tags

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3.1.3 Cleaning up Social Tags by Human Experts ........................................................ 35 3.1.4 Grouping Mood-related Social Tags ..................................................................... 35 3.2 MOOD CATEGORIES ................................................................................................ 36 3.3 COMPARISONS TO MUSIC PSYCHOLOGY MODELS ......................................... 38 3.3.1 Hevner’s Circle vs. Derived Categories ................................................................ 38 3.3.2 Russell’s Model vs. Derived Categories ............................................................... 40 3.3.3 Distances between Categories ............................................................................... 42 3.4 SUMMARY ................................................................................................................. 44 CHAPTER 4: CLASSIFICATION EXPERIMENT DESIGN ........................................ 46 4.1 EVALUATION METHOD AND MEASURE ............................................................ 46 4.1.1 Evaluation Task..................................................................................................... 46 4.1.2 Performance Measure and Statistical Test ............................................................ 47 4.2 CLASSIFICATION ALGORITHM AND IMPLEMENTATION .............................. 49 4.2.1 Supervised Learning and Support Vector Machines ............................................. 49 4.2.2 Algorithm Implementation .................................................................................... 51 4.3 SUMMARY ................................................................................................................. 52 CHAPTER 5: BUILDING A DATASET WITH TERNARY INFORMATION ........... 53 5.1 DATA COLLECTION ................................................................................................. 53 5.1.1 Audio Data ............................................................................................................ 53 5.1.2 Social Tags ............................................................................................................ 55 5.1.3 Lyric Data ............................................................................................................. 55 5.1.4 Summary ............................................................................................................... 56 5.2 GROUND TRUTH DATASET ................................................................................... 57 5.2.1 Identifying Mood Categories ................................................................................ 58 5.2.2 Selecting Songs ..................................................................................................... 61 5.2.3 Summary of the Dataset ........................................................................................ 65 CHAPTER 6: BEST LYRIC FEATURES ........................................................................ 68 6.1 TEXT AFFECT ANALYSIS ....................................................................................... 68 6.2 LYRIC FEATURES ..................................................................................................... 70 6.2.1 Basic Lyric Features.............................................................................................. 71 6.2.2 Linguistic Lyric Features ...................................................................................... 74 6.2.3 Text Stylistic Features ........................................................................................... 77 6.2.4 Lyric Feature Type Concatenations ...................................................................... 78 6.3 IMPLEMENTATION .................................................................................................. 80 6.4 RESULTS AND ANALYSIS ...................................................................................... 80 6.4.1 Best Individual Lyric Feature Types ..................................................................... 80 6.4.2 Best Combined Lyric Feature Types .................................................................... 81 6.4.3 Analysis of Text Stylistic Features ....................................................................... 84 6.5 SUMMARY ................................................................................................................. 87 CHAPTER 7: HYBRID SYSTEMS AND SINGLE-SOURCE SYSTEMS.................... 89 7.1 AUDIO FEATURES AND CLASSIFIER ................................................................... 89 7.2 HYBRID METHODS .................................................................................................. 90 7.2.1 Two Hybrid Methods ............................................................................................ 90 viii
7.2.2 Best Hybrid Method .............................................................................................. 92 7.3 LYRICS VS. AUDIO VS. HYBRID SYSTEMS ......................................................... 93 7.4 LYRICS VS. AUDIO ON INDIVIDUAL CATEGORIES .......................................... 97 7.4.1 Top Features in Content Word N-Grams .............................................................. 99 7.4.2 Top-Ranked Features Based on General Inquirer ............................................... 100 7.4.3 Top Features Based on ANEW and WordNet .................................................... 101 7.4.4 Top Text Stylistic Features ................................................................................. 102 7.4.5 Top Lyric Features in “Calm” ............................................................................. 103 7.5 SUMMARY ............................................................................................................... 104 CHAPTER 8: LEARNING CURVES AND AUDIO LENGTH .................................... 105 8.1 LEARNING CURVES ............................................................................................... 105 8.2 AUDIO LENGTHS .................................................................................................... 106 8.3 SUMMARY ............................................................................................................... 108 CHAPTER 9: CONCLUSIONS AND FUTURE RESEARCH ..................................... 110 9.1 CONCLUSIONS ........................................................................................................ 110 9.2 LIMITATIONS OF THIS RESEARCH ..................................................................... 113 9.2.1 Music Diversity ................................................................................................... 113 9.2.2 Methods and Techniques .................................................................................... 113 9.3 FUTURE RESEARCH .............................................................................................. 114 9.3.1 Feature Ranking and Selection ........................................................................... 114 9.3.2 More Classification Models and Audio Features ................................................ 114 9.3.3 Enrich Music Mood Theories ............................................................................. 115 9.3.4 Music of Other Types and in Other Cultures ...................................................... 115 9.3.5 Other Music-related Social Media Than Social Tags ......................................... 116 REFERENCES ................................................................................................................... 117 APPENDIX A: LYRIC REPETITION AND ANNOTATION PATTERNS ............... 125 APPENDIX B. PAPERS AND POSTERS ON THIS RESEARCH .............................. 129 ix
Page 1 and 2: IMPROVING MUSIC MOOD CLASSIFICATION
Page 3 and 4: concatenation and late fusion (line
Page 5 and 6: ACKNOWLEDGMENTS I would have never
Page 7: TABLE OF CONTENTS LIST OF FIGURES .
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.
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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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improving music mood classification using lyrics, audio and social tags

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