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IMPROVING MUSIC MOOD CLASSIFICATION
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concatenation and late fusion (line
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ACKNOWLEDGMENTS I would have never
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TABLE OF CONTENTS LIST OF FIGURES .
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7.2.2 Best Hybrid Method ..........
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LIST OF TABLES Table 3.1 Mood categ
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music itself and creating the socia
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the ultimate judge. Thus ground tru
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The advantages have attracted resea
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Research Question 2: Which type(s)
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There are two popular approaches in
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1.4.1 Contributions to Methodology
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experimental datasets in music mood
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CHAPTER 2: LITERATURE REVIEW 2.1 MO
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hypothesizes that there are at leas
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2.2.3 What We Know about Music Mood
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encoding facial expressions, some o
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environment by real-life users. The
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1) Tempo: also called “rhythmic p
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previous research did, Bischoff et
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other. Stylistic features, borrowed
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2.5 SUMMARY This chapter provided a
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“evaluation” containing 492 wor
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which it is derived. For instance,
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3.3 COMPARISONS TO MUSIC PSYCHOLOGY
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In total, 20 of the 36 derived cate
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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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- Page 79 and 80: CHAPTER 6: BEST LYRIC FEATURES This
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- Page 127 and 128: available, it is interesting to exp
- Page 129 and 130: Byrd, D., & Crawford, T. (2002). Pr
- Page 131 and 132: Hu, X., Sanghvi, V., Vong, B., On,
- Page 133 and 134: McEnnis, D., McKay, C., Fujinaga, I
- Page 135 and 136: Thayer, R. E. (1989). The Biopsycho
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