improving music mood classification using lyrics, audio and social tags

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5.1.2 Social Tags Since social tags on last.fm were used for identifying mood categories and the ground truth dataset will be built using a very similar method (see Section 5.2), last.fm is used for collecting social tags applied to the songs in the audio collections. For each song, the 100 most popular tags applied to it are provided by the last.fm API. The social tags used in building the dataset were collected during the month of February 2009, and 12,066 of the audio pieces had at least one last.fm tag. 5.1.3 Lyric Data Knees, Schedl, and Widmer (2005) extracted lyrics from the Internet by querying the Google search engine with keywords in the form “track name” + “artist name” + “lyrics,” but the results showed limited precision despite high recall. For this thesis research, precise lyrics are required, and thus lyrics were gathered from online lyric databases, instead of using general search engines. Lyricwiki.org was the primary resource because of its broad coverage and standardized format. Mldb.org was the secondary website which was consulted only when no lyrics were found on the primary database. To ensure data quality, the crawlers were implemented to use song title, artist and album information to identify the correct lyrics. In total, 8,839 songs had both social tags and lyrics. A language identification program 10 was then run against the lyrics, and 55 songs were identified and manually confirmed as non-English, leaving lyrics for 8,784 songs. The lyrics databases do not provide APIs for downloading. Hence one has to query the databases and download the displayed pages. This makes it a necessary step to clean up 10 Available at http://search.cpan.org/search%3fmodule=Lingua::Ident 55
irrelevant parts such as HTML markups and advertisements. In addition, lyrics need special preprocessing techniques because they have unique structures and characteristics. First, most lyrics consist of such sections as intro, interlude, verse, pre-chorus, chorus and outro, many with annotations on these segments. Second, repetitions of words and sections are extremely common. However, very few available lyric texts were found as verbatim transcripts. Instead, repetitions were annotated as instructions like “[repeat chorus 2x],” “(x5),” etc. Third, many lyrics contain notes about the song (e.g., “written by …”), instrumentation (e.g., “(SOLO PIANO)”), and/or the performing artists. In building a preprocessing program that takes these characteristics into consideration, the author manually identified about 50 repetition patterns and 25 annotation patterns (see Appendix A for a complete list). The program converted repetition instructions into the actual repeated segments for the indicated number of times while recognizing and removing other annotations. 5.1.4 Summary Table 5.2 summarizes the composition of the collected data. Table 5.2 Descriptions and statistics of the collections Collection Avg. length (second) Unique songs Have tags Have English Lyrics USPOP 253.6 8,271 7,301 6,948 USCRAP 243.5 2,553 456 237 American music 183.2 5,049 2,209 790 Metal music 311.8 105 105 104 Beatles 163.8 163 162 161 Magnatune 253.9 4,204 1,261 19 Assorted Pop 233.8 600 572 525 Total (Avg.) 234.8 20,945 12,066 8,784 56
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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
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 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: Table 5.1 Information of audio coll
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
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
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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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