improving music mood classification using lyrics, audio and social tags

More documents

Recommendations

Info

for lyrics, last.fm for tags, epinions.com for user reviews and amazon.com for images and editorial reviews (Hu & Wang, 2007). 1.5 SUMMARY This chapter introduced three important issues in music mood classification that motivated this dissertation research: the lack of a set of mood categories suitable for today's music listening environment, the prohibitively expensive cost involved in building ground truth datasets, and the premise of combining multiple resources in order to improve classification performances. Five research questions were proposed in this chapter. These questions are closely related and each is built upon the previous one. The answers to these questions will collectively shed light on the fundamental question of how lyrics, social tags and audio interact with one another with regard to music mood. Expected contributions of this research were summarized into three levels: methodology, evaluation and application. This research will contribute to the literature of music information retrieval, text affect analysis as well as music psychology. These related fields will be reviewed in the next chapter. 15
CHAPTER 2: LITERATURE REVIEW 2.1 MOOD AS A NOVEL METADATA TYPE FOR MUSIC Traditionally music information is organized and accessed by bibliographic metadata such as title, composer, performer and release date. However, recent MIR studies have pointed out that traditional bibliographic information of music is far from enough for effective MIR systems (Futrelle & Downie, 2002; Byrd & Crawford, 2002; Lee & Downie, 2004). Futrelle and Downie (2002) called for both descriptive and contextual metadata; Lee and Downie (2004) pointed out the importance of associative and perceptual metadata (e.g., use, mood, rating, etc.). In recent years, as online music repositories are becoming more popular, new non-traditional metadata have emerged in organizing music. Hu and Downie (2007) explored the 179 mood labels on AllMusicGuide 3 and analyzed the relationships that mood has with genre and artist. The results show that music mood, as a new type of music metadata, appears to be independent of genre and artist. Therefore, mood, as a new access point to music information, is a necessary complement to established ones. 2.2 MOOD IN MUSIC PSYCHOLOGY In the information science and MIR community, mood is a novel metadata type and thus there are many fundamental issues on music mood remaining unresolved. For example, there is no terminology consensus on the very topic in question: some researchers use “music emotion,” some others use “music mood” to refer to the affective aspects of music. On the other hand, there 3 http://www.allmusic.com, a popular metadata service that reviews and categorizes albums, songs and artists. 16
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: experimental datasets in music mood
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 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?