improving music mood classification using lyrics, audio and social tags

More documents

Recommendations

Info

It is somewhat surprising that the psychological feature indicating “hostile attitude or aggressiveness” (e.g., “devil,” “hate,” “kill”) was ranked at 134 among the 182 features. Although such individual words ranked high as content word features, the GI features were aggregations of certain kinds of words. By looking at rankings on specific words in General Inquirer, one can have a clearer understanding about which GI words were important. 7.4.3 Top Features Based on ANEW and WordNet According to Table 7.4, “ANEW” features significantly outperformed audio features on one category, “hopeful,” while “Affect-lex” features worked significantly better than audio features on categories “angry” and “hopeful.” Table 7.8 presents top-ranked features. Table 7.8 Top ANEW and Affect-lex features for categories where ANEW or Affect-lex significantly outperformed audio ANEW Affect-lex hopeful angry hopeful Average Valence score one wonderful Standard deviation (std) of Arousal scores baby sun Std of Dominance scores surprise loving Std of Valence scores care read Std of Arousal std death smile Std of Dominance std alive better Std of Valence std guilt heart Average Dominance std happiness lonely Average Arousal score hurt friend Average Valence std straight free Average Dominance score thrill found Average Arousal std cute strong suicide grow babe safe frightened god motherfucker girl down memory misery happy mad dream 101
Again, these top-ranked features seem to have strong semantic connections to the categories, and they share common words with the top-ranked features listed in Table 7.5 and Table 7.7. Although both Affect-lex and GI-lex are domain-oriented lexicons built from psycholinguistic resources, they contain different words, and thus each of them identified some novel features that are not shared by the other. The category “hopeful” is positioned at the center of Figure 5.2, with small values in both valence and arousal dimension, and thus it is not surprising that the top ANEW features for “hopeful” involve both valence and arousal scores. 7.4.4 Top Text Stylistic Features Text stylistic features performed the worst among all individual lyric feature types considered in this research (Table 6.4). In fact, the average accuracy of text stylistic features was significantly worse than each of the other feature types (p < 0.05). However, text stylistic features did outperform audio features in two categories: “hopeful” and “exciting.” Table 7.9 shows the top-ranked stylistic features (defined in Table 6.2) in these two categories. Table 7.9 Top-ranked text stylistic features for categories where text stylistics significantly outperformed audio hopeful stdLineLength uniqWordsPerLine avgWordLength repeatLineRatio avgLineLength repeatWordRatio numberOfUniqLines exciting uniqWordsPerLine avgRepeatWordRatioPerLine stdLineLength repeatWordRatio repeatLineRatio avgLineLength numberOfBlankLines Note how the top-ranked features in Table 7.9 are all text statistics without interjection words or punctuation marks. Also noteworthy is that these two categories both have relatively low positive valence values (but opposite arousal) as shown in Figure 5.2. 102
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 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: 7.4.2 Top-Ranked Features Based on
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

Create successful ePaper yourself

Delete template?

Save as template?