improving music mood classification using lyrics, audio and social tags

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5.2 GROUND TRUTH DATASET As discussed in Chapters 1 and 2, most previous experiments on music mood classification were conducted on small experiment datasets, and thus an efficient method is sorely needed for building ground truth datasets for music mood classification experimentation and evaluation. This section describes the process of building the experiment dataset for this research. McKay, McEnnis, and Fujinaga (2006) proposed a list of desired attributes of new music databases, based on which the author summarizes the following desired characteristics of a ground truth set for music mood classification: 1) There should be several thousand pieces of music in the ground truth set. Datasets with hundreds of songs used in previous experiments are too small to draw generalizable conclusions. 2) The mood categories cover most of the moods expressed by the collection of music being studied. The three to six categories used in most previous studies oversimplified the real question. 3) Each of the mood categories should represent a distinctive meaning. 4) One music piece can be labeled with multiple mood categories. This is more realistic than single-label classification, since a music piece may be “happy and calm,” “aggressive and depressed,” etc. 5) Each assignment of a mood label to a music piece is validated by multiple human judges. The more judges, the better it is. 57
Starting from the dataset collected by the procedure described in Section 5.1, i.e., the 8,784 songs with ternary information available, the author identified mood categories in this set of songs using similar method described in Section 3.1 and then selected songs for each of the categories. The following subsections describe the process in detail. 5.2.1 Identifying Mood Categories The mood categories identified in Section 3.2 are not directly applicable to labeling the dataset, because those categories were identified from the most popular social tags in last.fm. The songs associated with those most popular social tags might be very different from the songs available for this research. On the other hand, with the method described in Section 3.1, it is straightforward and efficient to derive mood categories that fit a given set of songs. In fact, it is the strength of this method to be able to efficiently derive mood categories for any set of songs with social tags available. The process started from the social tags applied to the 8,784 songs in the dataset via the last.fm API. There were 61,849 unique tags associated with these songs as of February 2009. WordNet-Affect was employed to filter out junk tags and tags with little or no affective meanings. Among the 61,849 unique tags, 348 were included in WordNet-Affect. However, these 348 words were not all mood-related in the music domain. Human expertise was applied to clean up these words. Just as in identifying mood categories from last.fm tags, the same two human experts identified and removed judgmental tags, ambiguous tags and tags with music meanings that did not involve an affective aspect. As a result of this step, 186 words remained. 58
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IMPROVING MUSIC MOOD CLASSIFICATION
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concatenation and late fusion (line
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ACKNOWLEDGMENTS I would have never
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TABLE OF CONTENTS LIST OF FIGURES .
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7.2.2 Best Hybrid Method ..........
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LIST OF TABLES Table 3.1 Mood categ
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music itself and creating the socia
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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
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: irrelevant parts such as HTML marku
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
Page 103 and 104: then compared to the feature concat
Page 105 and 106: According to the average accuracies
Page 107 and 108: systems and the audio-only system w
Page 109 and 110: Table 7.4 Accuracies of lyric and a
Page 111 and 112: 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
Page 117 and 118: 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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