improving music mood classification using lyrics, audio and social tags

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CHAPTER 8: LEARNING CURVES AND AUDIO LENGTH This chapter presents the experiments and results that answer research question 5: whether combining lyrics and audio can help reduce the amount of training data needed for effective classification, in terms of the number of training examples and audio length. 8.1 LEARNING CURVES Part of research question 5 is to find out whether lyrics can help reduce the number of training instances required for achieving certain performance levels. To answer this question, this research examines the learning curves of the single-source-based systems and the hybrid system using late fusion. In this experiment, in each fold of the 10-fold cross validation, the testing examples will be kept unchanged, while the training data sizes vary from 10% to 100% of all available training samples, with a 10% increment interval. The accuracies averaged across all categories are then used to draw the learning curves, which are presented in Figure 8.1. Figure 8.1 Learning curves of hybrid and single-source-based systems 105
Figure 8.1 shows a general trend that all system performances increased with more training data, but the performance of the audio-based system increased much more slowly than the other systems. With 20% training samples, the accuracies of the hybrid and the lyric-only systems were already better than the highest accuracy of the audio-only system with all possible amounts of training data. To achieve similar accuracy, the hybrid system needed about 20% fewer training examples than the lyric-only system. This validates the hypothesis that combining lyrics and audio can reduce required training examples needed to achieve certain classification performance levels. In addition, the learning curve of the audio-only system levels off (i.e., stops increasing) at 80% training sample size, while the curves of the other two systems never level off. This indicates the hybrid system and lyric-only system may further improve their performances if given more training examples. It is also worthy of notice that the performances of the lyric-only and audio-only systems drop at the points of 40% and 70% training examples respectively. This observation seems to contradict the general trend that performance increases with the amount of available training data. However, the performance differences between these points and their neighboring points are not statistically significant (at p < 0.05), and thus these performance drops can be seen as random effects and do not form a counter case of the general trend of the learning curves. 8.2 AUDIO LENGTHS The second part of research question 5 is about the effect of audio lengths on classification performance, and whether incorporating lyrics can reduce the requirement on the length of audio data for achieving certain performance levels. This research compares the performances of the audio-based, lyric-based and the late fusion hybrid system on datasets with audio clips of various 106
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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
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3.4 SUMMARY The identified categori
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CHAPTER 4: CLASSIFICATION EXPERIMEN
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accuracy = TP + TN TP + FN + FP + T
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Support Vector Machines (SVM), etc.
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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 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: However, some might argue that word
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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