improving music mood classification using lyrics, audio and social tags

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is a long history of influential studies in music psychology where these issues have been well studied. Hence, MIR researchers and information scientists who are interested in music mood should learn from music psychology literature on theoretical issues such as terminology and sources of music mood. This section reviews and summarizes important findings in representative studies on music and mood in music psychology. 2.2.1 Mood vs. Emotion Since the early stage of music psychology studies, researchers have paid attention to clarifying the concepts of mood and emotion. The most influential first work formally analyzing music and mood using psychological methodologies is probably Meyer’s Emotion and Meaning in Music (Meyer, 1956). In this book, Meyer stated that emotion is “temporary and evanescent” while mood is “relatively permanent and stable.” Sloboda and Juslin (2001) followed Meyer’s point after summarizing related studies during nearly a half century. In music psychology, both emotion and mood have been used to refer to the affective effects of music, but emotion seems to be more popular (Capurso et al., 1952; Juslin et al., 2006; Meyer, 1956; Schoen & Gatewood, 1927; Sloboda & Juslin, 2001). However, in MIR, researchers tend to choose mood over emotion (Feng, Zhuang, & Pan, 2003; Lu et al., 2006; Mandel et al., 2006; Pohle, Pampalk, & Widmer, 2005). In addition, existing music repositories also use mood rather than emotion as a metadata type for organizing music (e.g., AllMusicGuide and APM 4 ). While MIR researchers have yet to be formally interviewed on why they chose to use mood, the author 4 http://www.apmmusic.com It claims to be “the world's leading production music library and music services.” 17
hypothesizes that there are at least two reasons for MIR researchers to make a different choice from their colleagues in music psychology: First, as stated by Meyer, mood refers to a relatively long lasting and stable emotional state. While psychologists emphasize human responses to various stimuli of emotion, MIR researchers, at least at the current stage, are more interested in the general sentiment that music can convey. In other words, music psychologists focus on the very subjective responses to music which can be acute, momentary and fast changing, while the MIR community tries to find the common affective themes of music that are recognized by many people and are less volatile. Second, the research purposes of the two disciplines are different. Music psychologists want to discover why a human has emotional responses to music while MIR researchers want to find a new metadata type to organize and access music objects. The former focuses on a human’s responses, the latter focuses on music. It is the human who has emotion. Music does not have emotion, but it can carry a certain mood. Therefore, this research continues the choice of MIR researchers and adopts the term music mood rather than emotion. However, it is noteworthy that the two concepts are not absolutely detached. To some extent, their difference mainly lies in granularity. MIR researchers can still borrow insights from music psychology studies. In fact, when MIR technologies are developed to a level where individual and transitory affective responses become the subject of study, it is possible that the MIR community may change to adopt the notion of music emotion. 18
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: CHAPTER 2: LITERATURE REVIEW 2.1 MO
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.
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
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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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