improving music mood classification using lyrics, audio and social tags

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complementary to audio information, and thus it strives to improve the state of the art in automatic music mood classification by using audio, lyrics and social tags. 1.2 ISSUES IN MUSIC MOOD CLASSIFICATION In recent years, while more and more attention of MIR researchers has been drawn to music mood classification, several important issues have emerged, and resolving these issues becomes necessary for further progress on this topic. These issues are: mood categories, ground truth datasets and multi-modal systems. This dissertation attempts to respond to these issues by exploiting multiple information sources of music: lyrics, audio and social tags. 1.2.1 Mood Categories There are no existing standard mood categories either in MIR or in music psychology. Music psychologists have proposed a number of music mood models over the years, but the models generally lack the social context of music listening (Juslin & Laukka, 2004). It is unknown whether the models can fit well with today’s reality. On the other hand, MIR researchers have conducted experiments using different and small sets of mood categories. Besides the problem that these mood categories may have oversimplified the real problem in the real-life music listening environment, using different mood categories makes it hard to compare the performances across various classification approaches. 1.2.2 Ground Truth Datasets Besides mood categories, a dataset with ground truth labels is necessary for a scientific evaluation on music mood classification. As in many tasks in information retrieval, the human is 3
the ultimate judge. Thus ground truth datasets in existing experiments were mostly built by recruiting human assessors to manually label music pieces, and then selecting pieces with (near) consensus on human judgments. However, judgments on music are very subjective, and it is hard to achieve agreements across assessors (Skowronek, McKinney, & van de Par, 2006). This has seriously limited the sizes of experimental datasets and necessary validation on inter-assessor credibility. As a result, experimental datasets usually consist of merely several hundreds of music pieces, and each piece is judged by at most three human assessors, and in many cases, by only one assessor (e.g., Trohidis, Tsoumakas, Kalliris, & Vlahavas, 2008; Li & Ogihara, 2003; Lu, Liu, & Zhang, 2006). The situation is worsened by the intellectual property regulations on music materials, which effectively prevents sharing ground truth datasets with audio content among MIR researchers affiliated with different institutions. Therefore, it is clear that to enhance the development and evaluation in music mood classification, and in MIR research in general, a sound method is much in need to build ground truth sets of reliable quality in an efficient manner. 1.2.3 Multi-modal Classification Until recent years, MIR systems have focused on single-modal representation of music, mostly on audio content and some on symbolic scores. The seminal work of Aucouturier and Pachet (2004) pointed out that there appeared to be a “glass ceiling” in audio-based MIR, due to the fact that some high-level music features with semantic meanings might be too difficult to be derived from audio using current technology. Hence, researchers started paying attention to multi-modal classification systems that combine audio and text (e.g., Neumayer & Rauber, 2007; Aucouturier, Pachet, Roy, & Beurivé, 2007; Dhanaraj & Logan, 2005; Muller, Kurth, Damm, 4
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: music itself and creating the socia
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
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
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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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