improving music mood classification using lyrics, audio and social tags

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LIST OF FIGURES Figure 1.1 Research questions and experiment flow .................................................................... 12 Figure 2.1 Hevner's adjective cycle (Hevner, 1936) ..................................................................... 22 Figure 2.2 Russell’s model with two dimensions: arousal and valence (Russell, 1980) .............. 24 Figure 3.1 Words in Hevner’s circle that match tags in the derived categories ........................... 39 Figure 3.2 Words in Russell’s model that match tags in the derived categories .......................... 41 Figure 3.3 Distances of the 36 derived mood categories based on artist co-occurrences ............. 43 Figure 4.1 Binary classification .................................................................................................... 46 Figure 4.2 Support Vector Machines in a two-dimensional space ............................................... 51 Figure 5.1 Example of labeling a song using social tags .............................................................. 64 Figure 5.2 Distances between the 18 mood categories in the ground truth dataset ...................... 65 Figure 6.1 Distributions of “!” across categories .......................................................................... 86 Figure 6.2 Distributions of “hey” across categories ..................................................................... 86 Figure 6.3 Distributions of “ numberOfWordsPerMinute” across categories .............................. 87 Figure 7.1 Effect of α value in late fusion on average accuracy ................................................... 92 Figure 7.2 Box plots of system accuracies for the BEST lyric feature set ................................... 93 Figure 7.3 System accuracies across individual categories for the BEST lyric feature set .......... 96 Figure 8.1 Learning curves of hybrid and single-source-based systems .................................... 105 Figure 8.2 System accuracies with varied audio lengths ............................................................ 107 x
LIST OF TABLES Table 3.1 Mood categories derived from last.fm tags .................................................................. 36 Table 4.1 Contingency table of binary classification results ........................................................ 47 Table 5.1 Information of audio collections hosted in IMIRSEL .................................................. 54 Table 5.2 Descriptions and statistics of the collections ................................................................ 56 Table 5.3 Mood categories and song distributions ....................................................................... 60 Table 5.4 Distribution of songs with multiple labels .................................................................... 63 Table 5.5 Genre distribution of songs in the experiment dataset (“Other” includes genres occurring very infrequently such as “World,” “Folk,” “Easy listening,” and “Big band”) .......... 66 Table 5.6 Genre and mood distribution of positive examples ...................................................... 67 Table 6.1 Summary of basic lyric features ................................................................................... 73 Table 6.2 Text stylistic features evaluated in this research .......................................................... 78 Table 6.3 Summary of linguistic and stylistic lyric features ........................................................ 78 Table 6.4 Individual lyric feature type performances ................................................................... 81 Table 6.5 Best performing concatenated lyric feature types ......................................................... 82 Table 6.6 Performance comparison of “Content,” “FW,” “GI,” and “TextStyle”........................ 83 Table 6.7 Feature selection for TextStyle ..................................................................................... 84 Table 7.1 Comparisons on accuracies of two hybrid methods ..................................................... 93 Table 7.2 Accuracies of single-source-based and hybrid systems ................................................ 94 Table 7.3 Statistical tests on pair-wise system performances ....................................................... 95 Table 7.4 Accuracies of lyric and audio feature types for individual categories .......................... 98 Table 7.5 Top-ranked content word features for categories where content words significantly outperformed audio ....................................................................................................................... 99 Table 7.6 Top GI features for “aggressive” mood category ....................................................... 100 Table 7.7 Top-ranked GI-lex features for categories where GI-lex significantly outperformed audio ............................................................................................................................................ 100 Table 7.8 Top ANEW and Affect-lex features for categories where ANEW or Affect-lex significantly outperformed audio ................................................................................................ 101 Table 7.9 Top-ranked text stylistic features for categories where text stylistics significantly outperformed audio ..................................................................................................................... 102 Table 7.10 Top lyric features in “calm” category ....................................................................... 103 xi
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 and 8: TABLE OF CONTENTS LIST OF FIGURES .
Page 9: 7.2.2 Best Hybrid Method ..........
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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