improving music mood classification using lyrics, audio and social tags

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CHAPTER 1: INTRODUCTION 1.1 INTRODUCTION “Some sort of emotional experience is probably the main reason behind most people’s engagement with music” (Juslin & Sloboda, 2001). Nowadays people play music more often than ever, and the need for an easy way for daily users to search for music continues to rise. Research has been conducted to analyze similarities among music pieces based on which music can be organized in groups and recommended to users with suitable tastes. Until recently, most music classification studies focused on classifying music according to genre or artist style. The affective aspect of music (popularly known as music mood) has recently become yet another important criterion in classifying music. Music psychology research has disclosed that the affective aspects of music are important in defining “being musically similar” (Huron, 2000). The emotional component of music has been recognized as the most strongly associated with music expressivity (Juslin, Karlsson, Lindström, Friberg, & Schoonderwaldt, 2006), and research on music information behavior has also identified music mood as an important criterion used by people in music seeking and organization (Vignoli, 2004; Bainbridge, Cunningham, & Downie, 2003; Downie & Cunningham, 2002; Lee & Downie, 2004; Cunningham, Jones, & Jones, 2004; Cunningham, Bainbridge, & Falconer, 2006; to name a few). However, most existing music repositories do not support access to music by mood. In fact, music mood, due to its subjectivity, has been far from well studied in information science. Especially under the concept of Web 2.0, the general public can post their opinions on music pieces and thus yield collective wisdom augmenting value of 1
music itself and creating the social context of music seeking and listening. Although the affect aspect of music has long been studied in music psychology, such social context has not been considered. Therefore, music mood turns out to be a new metadata type for music. There has yet to be developed a suitable set of mood categories that can reflect the reality of music listening and can be well adopted in the music information retrieval (MIR) community. As the Internet and computer technologies enable people to access and share information on an unprecedentedly large scale, people call for automatic tools for music classification and recommendation. However, only a few existing music classification systems focus on mood. Most of them are solely based on the audio content 1 of the music, but mood classification also involves sociocultural aspects not extractable from audio using current audio technology. Studies have indicated lyrics and social tags are important in MIR. For example, Cunningham et al. (2006) reported lyrics as the most mentioned feature by respondents in answering why they hated a song. Geleijnse, Schedl, and Knees (2007) used social tags associated with music tracks to create a ground truth set for the task of artist similarity identification. Recently, researchers have started to exploit music lyrics in music classification (e.g., Laurier, Grivolla, & Herrera, 2008; Mayer, Neumayer, & Rauber, 2008) and hypothesize that lyrics, as a separate source from music audio, might be complementary to audio content. This dissertation research is also premised on the belief that lyrics and social tags would be 1 In this dissertation, “audio” in “audio content,” “audio-based” and “audio-only” refers to the audio media of music files such as .wav and .mp3 formats. In vocal music, singing of lyrics is recorded in the audio media files, but audio engineering technology has yet to be developed to correctly and reliably transcribe lyrics from media files, and thus “audio” in most MIR research is independent of lyrics. 2
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: LIST OF TABLES Table 3.1 Mood categ
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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