improving music mood classification using lyrics, audio and social tags

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ecommendation task where the algorithm automatically suggested mood or theme descriptors given social tags associated with a song. As contrast to a classification problem, the problem in Bischoff et al. (2009a) was a recommendation task where only the first N descriptors were evaluated (N = 3). 2.3.3 Music Mood Classification Combining Audio and Text The early work combining lyrics and audio in music mood classification can be traced back to Yang and Lee (2004) where they used both lyric bag-of-words features and the 182 psychological features proposed in the General Inquirer (Stone, 1966) to disambiguate categories that audio-based classifiers found confusing. Although the overall classification accuracy was improved by 2.1%, their dataset was too small (145 songs) to draw any reliable conclusions. Laurier et al. (2008) also combined audio and lyric bag-of-words features. Their experiments on 1,000 songs in four categories (also from Russell’s model) showed that the combined features with audio and lyrics improved classification accuracies in all four categories. Yang et al. (2008) evaluated both unigram and bigram bag-of-words lyric features as well as three methods for fusing lyric and audio sources on 1,240 songs in four categories (again from Russell’s model) and concluded that leveraging both lyrics and audio could improve classification accuracy over audio-only classifiers. As a very recent work, Bischoff et al. (2009b) combined social tags and audio in music mood and theme classification. The experiments on 1,612 songs in four and five mood categories showed that tag-based classifiers performed better than audio-based classifiers while the combined classifiers were the best. Again, it suggested that combining heterogeneous resources helped improve classification performances. Instead of concatenating two feature sets like most 27
previous research did, Bischoff et al. (2009b) combined the tag-based classifier and audio-based classifier via linear interpolation (one variation of late fusion). As their two classifiers were built with different classification models, it would not be reasonable to compare their single-sourcebased systems to a hybrid system built by feature concatenation. In this dissertation research, both audio-based and lyric-based classifiers use the same classification model so that it is feasible to compare the two commonly used fusion methods: feature concatenation and late fusion, using the same dataset. The aforementioned studies on music mood classification mostly used two to six mood categories which were most likely oversimplified and might not reflect the reality of the music listening environment, since the categories were mostly adapted from music psychology models and especially Russell’s model. Furthermore, the datasets were relatively small, which made their results hard to generalize. Finally, only a few of the most common lyric feature types were evaluated. It should also be noted that the performances of these studies were not comparable because they all used different datasets. 2.4 LYRICS AND SOCIAL TAGS IN MUSIC INFORMATION RETRIEVAL Audio-based approaches have seemed to dominate the field of MIR for the last decade. However, studies have started to take advantage of text, the ubiquitous media of information. This section reviews MIR studies that exploited lyrics and social tags in various tasks. 28
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: 1) Tempo: also called “rhythmic p
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 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
Page 103 and 104:
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
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
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There are other interesting observa
Page 121 and 122:
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
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
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18. “music fade” at end of a se
Page 139 and 140:
48. Segment annotation plus specifi
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