improving music mood classification using lyrics, audio and social tags

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Figure 3.1 Words in Hevner’s circle that match tags in the derived categories It also can be seen from Figure 3.1 that Clusters 2, 4, 6, 7 have the most matched words among all clusters, indicating Western popular songs (as the main music type in last.fm) mostly fall into these mood clusters. Besides exact matches, there are five categories in Table 3.1 with meanings close to some of the clusters in Hevner’s model: categories “angry” and “aggressive” are close to Cluster 8, the category “desire” is close to the “longing” and “yearning” in Cluster 3, and the category “earnest” is close to “serious” in Cluster 1. This use of different words for the same or similar meanings indicates a vocabulary mismatch between social tags and adjectives used in Clusters 3 and 8. Clusters 1 and 5 have the least matched or nearly matched words, reflecting that they are not good descriptors for Western popular songs. In fact, Hevner’s circle was mainly developed for classical music for which words in Clusters 1 and 5 (“light,” “delicate,” “graceful,” and “lofty”) would be a good fit. 39
In total, 20 of the 36 derived categories have at least one tag contained in Hevner’s circle or with close meanings to terms in Hevner’s circle. It is not surprising that the empirical data entailed more categories, since social tags were aggregated from millions of users while Hevner’s model was developed by studying merely hundreds of subjects. As a conclusion, after more than seven decades, Hevner’s circle is still largely in accordance with categories derived from today’s empirical music listening data. Admittedly, there are more mood categories in today’s empirical data, and there is a vocabulary mismatching issue, since language itself is evolving with time. 3.3.2 Russell’s Model vs. Derived Categories Figure 3.2 marks the words appearing in both Russell’s model and the derived sets of mood categories. In this figure, terms that match tags in the derived categories are marked with bold font and terms that have close meanings with tags in the derived categories are marked with italic font, with corresponding tags shown in parentheses. Words in the same derived categories are circled together. Figure 3.2 shows that 13 of the 28 words in Russell’s model match tags in the derived categories, and another three words have close meanings with tags in the derived categories. Hence, more than half of the words in Russell’s model match or nearly match tags in the derived categories. For those unmatched words, there are several cases: 1) Some words are synonyms according to WordNet, such as “content” and “satisfied,” “at ease” and “relaxed,” “droopy” and “tired,” “pleased” and “delighted.” Words in these pairs represent similar mood; 2) Some words are ambiguous and can be judgmental (“miserable,” “bored,” “annoyed”). If used as social tags, 40
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 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: 3.3 COMPARISONS TO MUSIC PSYCHOLOGY
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
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
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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
Page 113 and 114:
Again, these top-ranked features se
Page 115 and 116:
However, some might argue that word
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Figure 8.1 shows a general trend th
Page 119 and 120:
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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