- Page 1 and 2: IMPROVING MUSIC MOOD CLASSIFICATION
- Page 3: concatenation and late fusion (line
- 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 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
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3.4 SUMMARY The identified categori
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CHAPTER 4: CLASSIFICATION EXPERIMEN
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accuracy = TP + TN TP + FN + FP + T
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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