improving music mood classification using lyrics, audio and social tags

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moods while disgust, irritation, and dignity were of the lowest consistency. The implication for MIR is that some mood categories would be harder to classify than others. 5. There is some correspondence between listeners’ judgments on mood and musical parameters such as tempo, dynamics, rhythm, timbre, articulation, pitch, mode, tone attacks, and harmony (Sloboda & Juslin, 2001). Early experiments showed that the most important music element for excitement was a swift tempo; modality was important for sadness and happiness, but was useless for distinguishing excitement from calm; and melody played a very small part in producing a given affective state (Capurso et al., 1952). Schoen and Gatewood (1927) pointed out that the mood of amusement largely depended upon vocal music: “humorous description, ridiculous words, peculiarities of voice and manner are the most striking means of amusing people through music” (p. 163). This has been evidenced by the category, “humorous/silly/quirky” used in the AMC task in MIREX from 2007 to 2010. A subsequent examination of the AMC data found that music pieces manually labeled with this category primarily had the above-mentioned quality. Such correspondence between music mood and musical parameters has very important implications for designing and developing music mood classification algorithms. 2.2.4 Music Mood Categories Studies in psychology have proposed a number of models on human emotions, and music psychologists have adopted and extended a few influential models. The six “universal” emotions defined by Ekman (1982): anger, disgust, fear, happiness, sadness, and surprise, are well known in psychology. However, since they were designed for 21
encoding facial expressions, some of them may not be suitable for music (e.g., disgust), and some common music moods are missing (e.g., calm, soothing, and mellow). In music psychology, the earliest and still best-known systematic attempt at creating a music mood taxonomy was by Hevner (1936). Hevner designed an adjective circle of eight clusters of adjectives as shown in Figure 2.1, from which one can see: 1) the adjectives within each cluster are close in meaning; 2) the meanings of adjacent clusters would differ slightly; and, 3) the difference between clusters gets larger step by step until a cluster at the opposite position is reached. Figure 2.1 Hevner's adjective cycle (Hevner, 1936) Both Ekman’s and Hevner’s models belong to categorical models because the mood spaces consist of a set of discrete mood categories. Another well recognized kind of models is the dimensional models, where emotions are positioned in a continuous multidimensional space. The 22
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 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: 2.2.3 What We Know about Music Mood
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
Page 67 and 68: irrelevant parts such as HTML marku
Page 69 and 70: Starting from the dataset collected
Page 71 and 72: Table 5.3 Mood categories and song
Page 73 and 74: 1) a song has been tagged with one
Page 75 and 76: 5.2.2.4 Negative Samples Figure 5.1
Page 77 and 78: The patterns shown in this figure a
Page 79 and 80: CHAPTER 6: BEST LYRIC FEATURES This
Page 81 and 82: Liu (2006) identified discriminativ
Page 83 and 84:
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
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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
Page 131 and 132:
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
Page 139 and 140:
48. Segment annotation plus specifi
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