improving music mood classification using lyrics, audio and social tags

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(Juslin & Laukka, 2004). For this very reason, the mood categories identified from empirical data of social tags can be complementary and beneficial to music psychology research. To answer this question, a new method is proposed to derive mood categories from social tags, by combining the strength of linguistic resources and human expertise. The resultant mood categories are then compared to influential mood models proposed by music psychologists. Particularly, the following questions will be addressed in the comparison: 1) Is there any correspondence between the identified categories and the categories in psychological models? 2) Do the distances between the identified mood categories show similar patterns to those in the psychological models? Such comparison will disclose whether findings from social tags can be supported by the theoretical models and what the differences are between them. 1.3.2 Best Lyric Features Lyrics contain very rich information from which many types of features can be extracted. However, existing work on music mood classification that used lyric information only exploited the very basic, commonly used features such as content words and part-of-speech. To fill this intellectual gap, this dissertation examines and evaluates a wide range of lyric text features including the basic features used in previous studies, linguistic features derived from affect lexicons and psycholinguistic resources, as well as text stylistic features. The author attempts to determine the most useful lyric features by systematically comparing various lyric feature types and their combinations. 7
Research Question 2: Which type(s) of lyric features are the most useful in classifying music by mood? A variety of text features are extracted from lyrics. Details on the features are described in Section 6.2. As there is not enough evidence to hypothesize which feature type(s) or their combination would be the most useful, all feature types and combinations are evaluated in the task of mood classification, and their classification performances are compared using statistical tests (see Section 4.1). 1.3.3 Lyrics vs. Audio Previous studies have generally reported lyrics alone were not as effective as audio in music classification (e.g., Li & Ogihara, 2004; Mayer et al., 2008) and artist similarity identification (Logan, Kositsky, & Moreno, 2004). The third research question is to find out whether this is true for music mood classification with the lyric features that have not been previously evaluated. Research Question 3: Are there significant differences between lyric-based and audiobased systems in music mood classification, given both systems use the Support Vector Machines (SVM) classification model? The best performing lyric features determined in research question 2 are used to build a lyric-based mood classification system. It is then compared to the best performing audio-based system evaluated in the Audio Mood Classification (AMC) task in the Music Information Retrieval Evaluation eXchange (MIREX) 2007 and 2008. MIREX is a community-based framework for the formal evaluation of algorithms and techniques related to MIR development. 8
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: The advantages have attracted resea
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
Page 65 and 66: Table 5.1 Information of audio coll
Page 67 and 68: 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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