improving music mood classification using lyrics, audio and social tags

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2.4.1 Lyrics Despite being an important part of content of vocal music, lyrics have not been paid as much attention as its audio content counterpart. Scott and Matwin (1998) are often cited as the first MIR researchers exploiting lyrics. They conducted topic-wise text categorization using lyrics from more than 400 folk songs. Extending the traditional bag-of-words approach by integrating WordNet hypernyms (Fellbaum, 1998), the experiments showed that classification accuracies were improved over the approach using plain bag-of-words. In 2004, Logan et al. indexed the lyrics of 15,589 songs of 399 artists using the technique of Probabilistic Latent Semantic Analysis (PLSA) (Hofmann, 1999) in an attempt to determine artist similarity. Their experimental results showed that the lyric-based method was not as good as audio-based methods in the task of artist similarity, but the error analysis revealed that both methods made different errors and suggested that a combination of audio-based and lyric-based approaches would be a better technique. Researchers also studied lyrics for other tasks such as topic identification (Kleedorfer, Knees, & Pohle, 2008), language identification, structure extraction, theme categorization and similarity searches (Mahedero, Martinez, Cano, Koppenberger, & Gouyon, 2005). Other research combined lyrics and audio in a range of tasks, such as artist style detection (Li & Ogihara, 2004), genre classification (Neumayer & Rauber, 2007), hit song prediction (Dhanaraj & Logan, 2005) and music retrieval and navigation (Muller et al., 2007). However, all these studies used shallow text analysis and simple features such as bag-ofwords and part of speech (POS). As the most recent progress, Mayer et al. (2008) explored rhyme and stylistic features for the task of genre classification. Rhyme features in lyrics were defined as patterns distinguishing whether or not two subsequent lines in lyrics rhyme each 29
other. Stylistic features, borrowed from stylometric analysis (Rudman, 1998), included punctuations, digit counts, POS counts, words per line, unique words per line, unique words ratio, etc. The experiments showed that stylistic features were the best among individual lyric feature sets, but a combination of rhyme and stylistic features achieved the best performance in the task of genre classification. Both stylistic features alone and the combination of rhyme and stylistic features performed twice as well as the bag-of-words approach. The authors also compared results yielded with and without stemming, and no significant differences were found. As an unusual example of studies on music mood classification, Li and Ogihara (2004) compared several lyric feature types besides bag-of-words. They also borrowed wisdom in stylometric analysis and used function words, POS statistics and orthographic features of lexical items such as capitalization, word placement, word length, and line length. In predicting hit songs, Dhanaraj and Logan (2005) converted lyrics of each song to a vector using Probabilistic Latent Semantic Analysis (PLSA) (Hofmann, 1999). In PLSA, each dimension of the vector represents the likelihood that the song is about a pre-learned topic. Logan et al. (2004) showed, for the task of artist similarity, the topics learned using a lyrics corpus were better than those learned from other general corpus such as news. As shown from previous research on or using lyrics, bag-of-words features still dominate. Dimension reduction techniques and shallow linguistic features borrowed from stylometric analysis are also used. In addition, it is noteworthy that very few of the above studies compared performances of different feature types. 30
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 and 38: 1) Tempo: also called “rhythmic p
Page 39: previous research did, Bischoff et
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
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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
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
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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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