improving music mood classification using lyrics, audio and social tags

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5.2.3 Summary of the Dataset So far the experiment ground truth dataset has been built. There are 18 mood categories and each category has a number of positive examples and an equal number of negative examples. The relative distance between these 18 mood categories were calculated by co-occurrence of songs in the positive examples. That is, if two categories share a lot of positive songs, they should be similar. Figure 5.2 illustrates the distances of the 18 categories plotted in a twodimensional space using Multidimensional Scaling. In this figure, each category is represented by one tag in this category and a bubble whose size is proportional to the number of positive songs in this category. Figure 5.2 Distances between the 18 mood categories in the ground truth dataset 65
The patterns shown in this figure are similar to those found in Russell’s model as shown in Figure 2.2: 1) Categories placed together are intuitively similar; 2) Categories at opposite position represent contrast moods; and, 3) The horizontal and vertical dimensions correspond to valence and arousal respectively. Taken together, these similarities indicate that these 18 mood categories fit well with Russell’s mood model which is the most commonly used model in MIR mood classification research. In addition, it is interesting that both Figure 5.2 and Figure 3.3 show there are more sad songs than happy songs. Although this observation looks intuitively reasonable (e.g., most poems are sad rather than happy), further validation is needed from musicology and/or music psychology. The full dataset comprises 5,296 unique songs, including positive and negative examples. This number is much smaller than the total number of examples in all categories (which is 12,980) because categories often share samples. The decomposition of genres in this dataset is shown in Table 5.5 from which we can see most of the songs in this dataset are pop music. Table 5.5 Genre distribution of songs in the experiment dataset (“Other” includes genres occurring very infrequently such as “World,” “Folk,” “Easy listening,” and “Big band”) Genre No. of songs Genre No. of songs Genre No. of songs Rock 3,977 Reggae 55 Oldies 15 Hip Hop 214 Jazz 40 Other 35 Country 136 Blues 40 Unknown 564 Electronic 94 Metal 37 TOTAL 5,296 R & B 64 New Age 25 To have a clear look at the relationship between mood and genre, Table 5.6 summarizes how the 6,490 positive examples distribute across different genres and moods. Although for this dataset all moods are dominated by Rock songs, there are still observations that comply with 66
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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
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 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: 5.2.2.4 Negative Samples Figure 5.1
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 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.
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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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