improving music mood classification using lyrics, audio and social tags

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conclusively made. Therefore, the answer to research question 3 would be: the lyric-based system outperformed the audio-based system in terms of average accuracy across all 18 mood categories in the experiment dataset, but their difference was just shy of being accepted as statistically significant. Table 7.3 Statistical tests on pair-wise system performances Feature set Better system Worse system p value BEST Hybrid (late fusion) Audio-only 0.001 BEST Hybrid (feature concatenation) Audio-only 0.027 BEST Lyric-only Audio-only 0.054 BEST Hybrid (late fusion) Lyric-only 0.110 BEST Hybrid (feature concatenation) Lyric-only 0.517 BEST Hybrid (late fusion) Hybrid (feature concatenation) 0.327 ANEW+TextStyle Hybrid (late fusion) Audio-only 0.004 ANEW+TextStyle Hybrid (feature concatenation) Audio-only 0.045 ANEW+TextStyle Lyric-only Audio-only 0.074 ANEW+TextStyle Hybrid (late fusion) Lyric-only 0.217 ANEW+TextStyle Hybrid (late fusion) Hybrid (feature concatenation) 0.569 ANEW+TextStyle Lyric-only Hybrid (feature concatenation) 0.681 The statistical tests presented on Table 7.3 also show that both hybrid systems using late fusion and feature concatenation were significantly better than the audio-only system at p < 0.05. In particular, the hybrid systems with late fusion improved accuracy over the audio-only system by 9.6% and 8% for the top two lyric feature sets respectively (Table 7.2). These demonstrate the usefulness of lyrics in complementing music audio in the task of mood classification. However, the differences between the hybrid systems and the lyric-only system were not statistically significant (p = 0.11 and 0.217 for the late fusion system and p = 0.517 and 0.681 for the feature concatenation system). Therefore, the answer to research question 4 is: systems combining lyrics and audio outperformed systems based on either lyric-only or audio-only in terms of average accuracy across all 18 mood categories in the experiment dataset. The difference between hybrid 95
systems and the audio-only system was statistically significant, but the difference between hybrid systems and the lyric-only system was not statistically significant. Figure 7.3 shows the system accuracies across individual mood categories for the BEST lyric feature set where the categories are in descending order of the number of songs in each category. Figure 7.3 System accuracies across individual categories for the BEST lyric feature set Figure 7.3 reveals that system performances become more erratic and unstable after the category “cheerful.” Those categories to the right of “cheerful” have fewer than 142 positive examples. This suggests that the systems are vulnerable to the data scarcity problem. For some of the smaller categories, system performances were even lower than baseline performance (50% for binary classification). This is a somewhat expected result as the lengths of the feature vectors 96
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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
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 “!”
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: According to the average accuracies
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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