improving music mood classification using lyrics, audio and social tags

More documents

Recommendations

Info

The 186 words were then grouped into 49 groups using the synset structure in WordNet. Tags in each group were synonyms according to WordNet. After that, the two experts further merged several tag groups which were deemed musically similar. For instance, the group of (“cheer up,” “cheerful”) was merged with (“jolly,” “rejoice”); (“melancholic,” “melancholy”) was merged with (“sad,” “sadness”). This resulted in 34 tag groups, each representing a mood category for this dataset. Finally, the author manually screened a number of tags that did not exactly match words in WordNet-Affect but were most frequently applied to the songs in the dataset. Some of those tags had exactly the same meaning as matched words in WordNet-Affect and thus were added into corresponding categories. For instance, “sad song” and “feeling sad” were added into the category of (“sad,” “sadness”); “mood: happy” and “happy songs” were added into the category of (“happy,” “happiness”). In addition, there were some very popular tags with affect meanings in the music domain but were not included in WordNet-Affect, such as “mellow” and “upbeat.” The experts recommended including these tags in the categories of the same meaning. For example, “mellow” was added to the (“calm,” “quiet”) category, and “upbeat” was added to the category of (“gleeful,” “high spirits”). For the classification experiments, each category should have enough samples to build classification models. Thus, categories with fewer than 30 songs were dropped, resulting in 18 mood categories containing 135 tags. These categories and their member tags were then validated for reasonableness by a number of native English speakers. Table 5.3 lists the categories, their member tags and number of songs in each category (see next subsection). 59
Table 5.3 Mood categories and song distributions Categories calm, comfort, quiet, serene, mellow, chill out, calm down, calming, chillout, comforting, content, cool down, mellow music, mellow rock, peace of mind, quietness, relaxation, serenity, solace, soothe, soothing, still, tranquil, tranquility, tranquillity Number of tags Number of songs 25 1,680 sad, sadness, unhappy, melancholic, melancholy, feeling sad, mood: sad – slightly, sad song 8 1,178 glad, happy, happiness, happy songs, happy music, mood: happy 6 749 romantic, romantic music 2 619 gleeful, upbeat, high spirits, zest, enthusiastic, buoyancy, elation, mood: upbeat gloomy, depressed, blue, dark, depressive, dreary, gloom, darkness, depress, depression, depressing 8 543 11 471 angry, anger, choleric, fury, outraged, rage, angry music 7 254 mournful, grief, heartbreak, sorrow, sorry, doleful, heartache, heartbreaking, heartsick, lachrymose, mourning, plaintive, regret, sorrowful 14 183 dreamy 1 146 cheerful, cheer up, festive, jolly, jovial, merry, cheer, cheering, cheery, get happy, rejoice, songs that are cheerful, sunny 13 142 brooding, contemplative, meditative, reflective, broody, pensive, pondering, wistful 8 116 aggressive, aggression 2 115 anxious, angst, anxiety, jumpy, nervous, angsty 6 80 confident, encouraging, encouragement, optimism, optimistic 5 61 hopeful, desire, hope, mood: hopeful 4 45 earnest, heartfelt 2 40 cynical, pessimism, pessimistic, weltschmerz, cynical/sarcastic 5 38 exciting, excitement, exhilarating, thrill, ardor, stimulating, thrilling, titillating 8 30 TOTAL 135 6,490 60
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 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: Starting from the dataset collected
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
Page 119 and 120: There are other interesting observa
Page 121 and 122:
CHAPTER 9: CONCLUSIONS AND FUTURE R
Page 123 and 124:
different strengths. Lyric-based sy
Page 125 and 126:
close relationship with music mood.
Page 127 and 128:
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
Page 137 and 138:
18. “music fade” at end of a se
Page 139 and 140:
48. Segment annotation plus specifi
show all

improving music mood classification using lyrics, audio and social tags

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?