improving music mood classification using lyrics, audio and social tags

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most influential ones contain such dimensions as valence (happy-unhappy), arousal (activeinactive), and dominance (dominant-submissive) (Mehrabian, 1996; Russell, 1980; Thayer, 1989). However, there is no consensus on how many dimensions there should be and which dimensions to consider. For example, a well cited study by Wedin (1972) identified three dimensions: intensity-softness, pleasantness-unpleasantness and solemnity-triviality, while another study by Asmus (1995) found nine dimensions: evil, sensual, potency, humor, pastoral, longing, depression, sedative, and activity. Among all these dimensional models, Russell’s model of the combination of valence and arousal dimensions (Russell, 1980) has been adopted in a few experimental studies in music psychology (e.g., Schubert, 1996; Tyler, 1996), and MIR researchers have been using similar taxonomies based on this model (e.g., Kim, Schmidt, & Emelle, 2008; Laurier et al., 2008; Lu et al., 2006). As shown in Figure 2.2, the original Russell’s model places 28 emotion denoting adjectives on a circle in a bipolar space consisting of valence and arousal dimensions. In fact, categorical models and dimensional models cannot be completely separated. Gabrielsson and Lindström (2001) argued that Hevner’s model suggested an implicit dimensionality similar to the combination of valence (cluster 2 – cluster 6) and arousal (clusters 7/8 – clusters 4/3). All these psychological models were proposed in laboratory settings and thus were criticized as having a lack of social context of music listening (Juslin & Laukka, 2004). Therefore, this dissertation research promises that a set of music mood categories derived from social tags would be rich in social context, since social tags are posted in the most natural music listening 23
environment by real-life users. The identified mood categories are compared to both categories in Hevner’s model and those in Russell’s model (see Section 3.3). Figure 2.2 Russell’s model with two dimensions: arousal and valence (Russell, 1980) 2.3 MUSIC MOOD CLASSIFICATION 2.3.1 Audio-based Music Mood Classification Most existing work on automatic music mood classification is exclusively based on audio features among which timbral and rhythmic features are the most popular across studies (e.g., Lu et al., 2006; Pohle et al., 2005; Trohidis et al., 2008). The datasets used in these experiments usually consisted of several hundred to a thousand songs labeled with four to six mood categories. 24
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 and 32: 2.2.3 What We Know about Music Mood
Page 33: encoding facial expressions, some o
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
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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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