improving music mood classification using lyrics, audio and social tags

More documents

Recommendations

Info

in music psychology. In general, the comparison of the mood categories derived from social tags to those in psychological models establishes an example of refining and/or adapting theories and models to better fit the reality of users’ information behaviors. Text affect analysis has been an active research topic in text mining in recent years (Pang & Lee, 2008), but has just started being applied to the music domain. Many of the lyric text features examined in this dissertation have never been formally studied in the context of music mood classification. Similarly, most of the feature type combinations have never previously been compared to each other using a common dataset. Thus, this dissertation research advances the state of text affect analysis in the music domain. Fusion methods have recently started being used in combining multiple sources in music classifications, but different fusion methods have rarely been compared on a common dataset. This dissertation compares two fusion methods, and the result provides suggestions for future research in music mood classification. 1.4.2 Contributions to Evaluation As mentioned before, an effective and scalable evaluation approach is much needed in the music domain. In this dissertation, a large ground truth dataset is built from social tags without recruiting human assessors (see Section 5.2). The proposed method of deriving ground truth from social tags can help reduce the prohibitive cost of human assessments and clear the way to large scale experiments in MIR. The ground truth dataset built for this study is unique. It contains 5,296 unique songs in 18 mood categories with mood labels given by a number of real-life users. This is one of the largest 13
experimental datasets in music mood classification with ternary information sources available: audio, lyrics and social tags. Part of the dataset has been made available to the MIR community through the 2009 and 2010 iterations of the MIREX. Serving as a testbed accessible to the entire MIR community, this dataset helps facilitate the development and comparison of new techniques in music mood classification. 1.4.3 Contributions to Application Music mood classification and recommendation systems are direct applications of this dissertation research. Based on the findings, one can plug in existing tools on text categorization, audio feature extraction and fusion methods to build a system that combines audio and text in an optimized way. Moody is an online prototype of such applications (Hu et al., 2008b). It recommends music in similar moods and classifies users’ songs on-the-fly. The answers to research question 5 on learning curves and audio length give a practical reference on whether combining lyrics and audio can reduce the number of needed training examples and the length of audio a system has to process. Training examples are expensive to obtain and audio processing is computationally complex. Therefore, answers to this research question may help improve the efficiency of music mood classification systems. In this research, lyrics and social tags associated with songs are collected from various Web services such as lyrics databases and music sharing/tagging sites. This is an example of applications collecting and integrating information from heterogeneous resources. During a pilot study of this dissertation research, a prototype Web search system has been developed to crawl and integrate complementary information of albums from multiple websites, including mldb.org 14
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: 1.4.1 Contributions to Methodology
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 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?