improving music mood classification using lyrics, audio and social tags

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ABSTRACT The affective aspect of music (popularly known as music mood) is a newly emerging metadata type and access point to music information, but it has not been well studied in information science. There has yet to be developed a suitable set of mood categories that can reflect the reality of music listening and can be well adopted in the Music Information Retrieval (MIR) community. As music repositories have grown to an unprecedentedly large scale, people call for automatic tools for music classification and recommendation. However, there have been only a few music mood classification systems with suboptimal performances, and most of them are solely based on the audio content of the music. Lyric text and social tags are resources independent of and complementary to audio content but have yet to be fully exploited. This dissertation research takes up these problems and aims to 1) summarize fundamental insights in music psychology that can help information scientists interpret music mood; 2) identify mood categories that are frequently used by real-world music listeners, through an empirical investigation of real-life social tags applied to music; 3) advance the technology in automatic music mood classification by a thorough investigation on lyric text analysis and the combination of lyrics and audio. Using linguistic resources and human expertise, 36 mood categories were identified from the most popular social tags collected from last.fm, a major Western music tagging site. A ground truth dataset of 5,296 songs in 18 mood categories were built with mood labels given by a number of real-life users. Both commonly used text features and advanced linguistic features were investigated, as well as different feature representation models and feature combinations. The best performing lyric feature sets were then compared to a leading audio-based system. In combining lyric and audio sources, both methods of feature ii
concatenation and late fusion (linear interpolation) of classifiers were examined and compared. Finally, system performances on various numbers of training examples and different audio lengths were compared. The results indicate: 1) social tags can help identify mood categories suitable for a real world music listening environment; 2) the most useful lyric features are linguistic features combined with text stylistic features; 3) lyric features outperform audio features in terms of averaged accuracy across all considered mood categories; 4) systems combining lyrics and audio outperform audio-only and lyric-only systems; 5) combining lyrics and audio can reduce the requirement on training data size, both in number of examples and in audio length. Contributions of this research are threefold. On methodology, it improves the state of the art in music mood classification and text affect analysis in the music domain. The mood categories identified from empirical social tags can complement those in theoretical psychology models. In addition, many of the lyric text features examined in this study have never been formally studied in the context of music mood classification nor been compared to each other using a common dataset. On evaluation, the ground truth dataset built in this research is large and unique with ternary information available: audio, lyrics and social tags. Part of the dataset has been made available to the MIR community through the Music Information Retrieval Evaluation eXchange (MIREX) 2009 and 2010, the community-based evaluation framework. The proposed method of deriving ground truth from social tags provides an effective alternative to the expensive human assessments on music and thus clears the way to large scale experiments. On application, findings of this research help build effective and efficient music mood classification and recommendation systems by optimizing the interaction of music audio and lyrics. A prototype of such systems can be accessed at http://moodydb.com. iii
Page 1: IMPROVING MUSIC MOOD CLASSIFICATION
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 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
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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
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Table 5.1 Information of audio coll
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irrelevant parts such as HTML marku
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Starting from the dataset collected
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Table 5.3 Mood categories and song
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1) a song has been tagged with one
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5.2.2.4 Negative Samples Figure 5.1
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The patterns shown in this figure a
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CHAPTER 6: BEST LYRIC FEATURES This
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Liu (2006) identified discriminativ
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as feature values respectively. The
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6.2.2 Linguistic Lyric Features In
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mixed with regard to its usefulness
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Table 6.2 Text stylistic features e
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6.3 IMPLEMENTATION The Snowball ste
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shows the best combined feature set
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6.4.3 Analysis of Text Stylistic Fe
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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
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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
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Table 7.4 Accuracies of lyric and a
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7.4.2 Top-Ranked Features Based on
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Again, these top-ranked features se
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However, some might argue that word
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Figure 8.1 shows a general trend th
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There are other interesting observa
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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
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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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