improving music mood classification using lyrics, audio and social tags

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Except for the combination of ANEW and TextStyle, all of the other top performing feature combinations shown in Table 6.5 are concatenations of four or more feature types, and thus have very high dimensionality. In contrast, ANEW+TextStyle has only 37 dimensions, which is certainly a lot more efficient than the others. On the other hand, high dimensionality provides room for feature selection and reduction. Indeed, a previous study of the author (Hu et al., 2009a) applied three feature selection methods on basic unigram lyric features (i.e., F-Score, SVM score and language model comparisons) and showed improved performances. It is a future research direction to investigate feature selection and reduction for feature combinations with high dimensionality. Except for ANEW+TextStyle, all other top performing feature concatenations contain the combination of “Content,” “FW,” “GI,” and “TextStyle.” The relative importance of the four individual feature types can be revealed by comparing the combinations of any three of the four types. As shown in Table 6.6, the combination of FW + GI + TextStyle performed the worst. Together with the fact that Content performed the best among all individual feature types, it is safe to conclude that content words are still very important in the task of lyric mood classification. Table 6.6 Performance comparison of “Content,” “FW,” “GI,” and “TextStyle” Type Accuracy Content+FW+TextStyle 0.632 Content+FW+GI 0.631 Content+GI+TextStyle 0.624 FW+GI+TextStyle 0.619 83
6.4.3 Analysis of Text Stylistic Features As shown in the results, TextStyle is a very interesting feature type. It captures very different characteristics from the lyrics than other feature types, i.e., TextStyle is orthogonal to other feature types. This subsection takes a closer look at TextStyle to determine the most important features within this type. The specific features in TextStyle are listed in Table 6.2. The interjection words and punctuation marks were selected using a series of experiments. Classification performances using varied numbers of top-ranked features are compared in Table 6.7, with the row of best performances marked as bold. Table 6.7 Feature selection for TextStyle Number of features Accuracy TextStats I&P Total TextStyle ANEW+TextStyle 17 0 17 0.524 0.634 17 8 25 0.529 0.637 17 15 32 0.526 0.632 17 25 42 0.514 0.631 17 45 62 0.514 0.628 17 75 92 0.513 0.615 17 134 151 0.513 0.612 Initially all common punctuation marks and interjection words 16 were considered. There are 134 of them. Then the interjection words and punctuation marks (denoted as “I&P” in Table 6.7) were ranked according to their SVM weights (see below), and the n most important ones were 16 The list of English interjection words was based on the one obtained from http://www.english-grammarrevolution.com/list-of-interjections.html 84
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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
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
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: shows the best combined feature set
Page 97 and 98: Figure 6.1 Distributions of “!”
Page 99 and 100: stylistic features appeared to be a
Page 101 and 102: Support Vector Machines (SVM) as it
Page 103 and 104: then compared to the feature concat
Page 105 and 106: According to the average accuracies
Page 107 and 108: systems and the audio-only system w
Page 109 and 110: Table 7.4 Accuracies of lyric and a
Page 111 and 112: 7.4.2 Top-Ranked Features Based on
Page 113 and 114: Again, these top-ranked features se
Page 115 and 116: However, some might argue that word
Page 117 and 118: Figure 8.1 shows a general trend th
Page 119 and 120: There are other interesting observa
Page 121 and 122: CHAPTER 9: CONCLUSIONS AND FUTURE R
Page 123 and 124: different strengths. Lyric-based sy
Page 125 and 126: close relationship with music mood.
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
Page 137 and 138: 18. “music fade” at end of a se
Page 139 and 140: 48. Segment annotation plus specifi
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