improving music mood classification using lyrics, audio and social tags

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Table 3.1 (cont.) Categories Number of tags brooding, broody, contemplative, meditative, pensive, reflective, wistful 7 confident, encouragement, encouraging, fearless, optimism, optimistic 6 angry, anger, furious, fury, rage 5 anxious, angst, anxiety, jumpy, nervous 5 exciting, exhilarating, stimulating, thrill, thrilling 5 cynical, misanthropic, misanthropy, pessimistic 4 compassionate, mercy, pathos, sympathy 4 desolate, desolation, isolation, loneliness 4 scary, fear, panic, terror 4 hostile, hatred, malevolent, venom 4 sad, melancholic, sadness 3 desperate, despair, hopeless 3 tender, caring, tenderness 3 glad, happiness, happy 3 hopeful, desire, hope 3 earnest, heartfelt 2 aggression, aggressive 2 adoration, worshipful 2 hysterical, hysteria 2 disturbing, distress 2 jealousy, envy 2 hectic, restless 2 dreamy 1 romantic 1 suspense 1 awe 1 surprising 1 frustration 1 satisfaction 1 carefree 1 triumphant 1 TOTAL 136 37
3.3 COMPARISONS TO MUSIC PSYCHOLOGY MODELS In this section, the identified mood categories are compared to both Hevner’s categorical model and Russell’s two-dimensional model, with regard to the following two aspects: 1) Is there any correspondence between the identified categories and those in the psychological models? 2) Do the distances between mood categories show similar patterns to those in the psychological models? 3.3.1 Hevner’s Circle vs. Derived Categories Some of the terms in Hevner’s circle (Figure 2.1) are known to be old-fashioned and are rarely used for describing moods nowadays. This is reflected by the fact that only 37 of the 66 words in Hevner’s circle were found in WordNet-Affect, including matches of terms in different derived forms (e.g., “solemnity” and “solemn” were counted as a match). By comparing the clusters in Hevner’s circle to the set of categories identified from social tags, it was found that 23 words (35% of all) in Hevner’s circle matched tags in the derived categories, as indicated in Figure 3.1, where matched words are surrounded by rectangles. Please note that in Figure 3.1 the order of words within each cluster may be changed from Figure 2.1, so that words in the same derived categories are within one rectangle. The observation that the rectangles never cross Hevner’s clusters suggests that the boundaries of Hevner’s clusters and derived categories are in accordance with each other, despite the finer granularity of the derived categories. 38
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 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: which it is derived. For instance,
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
Page 85 and 86: 6.2.2 Linguistic Lyric Features In
Page 87 and 88: mixed with regard to its usefulness
Page 89 and 90: Table 6.2 Text stylistic features e
Page 91 and 92: 6.3 IMPLEMENTATION The Snowball ste
Page 93 and 94: shows the best combined feature set
Page 95 and 96: 6.4.3 Analysis of Text Stylistic Fe
Page 97 and 98: 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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