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content word features, but features without stemming had higher averaged accuracy. The best performance of each individual feature type is presented in Table 6.4. For individual feature types, the best performing one was Content, the bag-of-words features of content words with multiple orders of n-grams. Individual linguistic feature types did not perform as well as Content. In addition, among linguistic feature types, bag-of-words features (i.e., GI-lex and Affect-lex) were the best. The poorest performing feature types were ANEW and TextStyle, both of which were statistically different from the other feature types (at p < 0.05). There was no statistically significant difference among the remaining feature types. Table 6.4 Individual lyric feature type performances Feature Abbreviation Feature Type Representation Accuracy Content uni+bi+trigrams of content words Boolean 0.617 Cont-stem uni+bi+trigrams of stemmed content words tfidf 0.613 GI-lex words in GI Boolean 0.596 Affect-lex words in expanded ANEW and WordNet-Affect tfidf 0.594 FW uni+bi+trigrams of function words Boolean 0.594 GI GI psychological features tfidf 0.586 POS uni+bi+trigrams of part-of-speech Boolean 0.579 ANEW scores in expanded ANEW - 0.545 TextStyle text stylistic features - 0.529 6.4.2 Best Combined Lyric Feature Types The best individual feature types (shown in Table 6.4 excluding “Cont-stem”) were concatenated with one another, resulting in 255 combined feature types. Because value ranges of the feature types varied a great deal (e.g., some are counts, others are normalized weights, etc.), all feature values were normalized to the interval of [0, 1] prior to concatenation. Table 6.5 81
shows the best combined feature sets among which there was no significant difference (at p < 0.05). The best performing feature combination was Content + FW + GI + ANEW + Affect-lex + TextStyle which achieved an accuracy 2.1% higher than the best individual feature type, Content (0.638 vs. 0.617). All of the best performing lyric feature type concatenations listed in Table 6.5 contain certain linguistic features and text stylistic features (“TextStyle”), although TextStyle performed the worst among all individual feature types (as shown in Table 6.4). This indicates that TextStyle must have captured very different characteristics of the data than other feature types and thus could be complementary to others. The top three feature combinations also contain ANEW scores, and ANEW scores alone was also significantly worse than other individual feature types (at p < 0.05). It is interesting to see that the two poorest performing feature types scored second best (with no statistically significant difference from the best) when combined with each other. In addition, the ANEW and TextStyle feature types are the only two types that do not conform to the bag-of-words framework among all of the eight individual feature types. Table 6.5 Best performing concatenated lyric feature types Type Number of dimensions Accuracy Content+FW+GI+ANEW+Affect-lex+TextStyle 107,360 0.638 ANEW+TextStyle 37 0.637 Content+FW+GI+GI-lex+ANEW+Affect-lex+TextStyle 115,675 0.637 Content+FW+GI+GI-lex+TextStyle 107,907 0.636 Content+FW+GI+Affect-lex+TextStyle 107,348 0.636 Content+FW+GI+TextStyle 99,592 0.635 82
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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
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.
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: 6.3 IMPLEMENTATION The Snowball ste
Page 95 and 96: 6.4.3 Analysis of Text Stylistic Fe
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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