improving music mood classification using lyrics, audio and social tags

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Its results reflect the best results for the tasks (Downie, 2008). The performance of a top-ranked system in the AMC tasks sets a difficult baseline against which comparisons must be made. All audio-based classification systems in MIREX as well as systems in most other music classification studies applied standard supervised learning models such as K-Nearest Neighbor (KNN), Naïve Bayes, and Support Vector Machines (SVM). Among the learning models, SVM seems the most popular model with top performance. This dissertation uses SVM as the classification model for two reasons: 1) the selected audio-based system uses SVM; and 2) SVM achieved the best or close to the best results in both MIR and text categorization experiments in general (Mandel, Poliner, & Ellis, 2006; Hu, Downie, Laurier, Bay, & Ehmann 2008a; Tzanetakis & Cook, 2002; Yu, 2008). 1.3.4 Combining Lyrics and Audio In machine learning, it is acknowledged that multiple independent sources of features will likely compensate for one another, resulting in better performances than approaches using any one of the sources (Dietterich, 2000). Previous work in music classification has used such hybrid sources as audio and lyrics (e.g., Mayer et al., 2008), audio and symbolic scores (e.g., McKay & Fujinaga, 2008), etc., and has shown improved performance. Thus, one hypothesis in this dissertation is that hybrid systems combining audio and lyrics perform better than systems using either source. Research Question 4: Are systems combining audio and lyrics significantly better than audio-only or lyric-only systems? 9
There are two popular approaches in assembling hybrid systems (also called “fusion methods”). The most straightforward one is feature concatenation where two feature sets are concatenated and the classification algorithms run on the combined feature vectors (e.g. Laurier et al., 2008; Mayer et al., 2008). The other method is often called “late fusion” which combines the outputs of individual classifiers based on different sources, either by (weighted) averaging (e.g., Bischoff et al., 2009b; Whitman & Smaragdis, 2002) or by multiplying (e.g., Li & Ogihara, 2004). To answer this research question, both fusion methods are implemented, and the performances of hybrid systems and systems based on single sources are compared using statistical tests. 1.3.5 Learning Curves and Audio Lengths A learning curve describes the relationship between classification performance and the number of training examples. Usually performance increases with the number of training examples, and the point where performance stops increasing indicates the minimum number of training examples needed for achieving the best performance. In addition to classification performances, the learning curve is also an important measure for the effectiveness of a classification system. Therefore, the comparison on learning curves of the hybrid systems and single-source-based systems can reveal whether combining lyrics and audio helps reduce the number of training examples needed for achieving comparable or better performances as audioonly or lyric-only systems. Due to the time complexity of audio processing, MIR systems often process the x second audio clips truncated from the middle of the original tracks instead of the complete tracks, where x often equals 30 or 15. As text processing is much faster than audio processing, it is of practical 10
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: Research Question 2: Which type(s)
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
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Table 5.3 Mood categories and song
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1) a song has been tagged with one
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5.2.2.4 Negative Samples Figure 5.1
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The patterns shown in this figure a
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CHAPTER 6: BEST LYRIC FEATURES This
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Liu (2006) identified discriminativ
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as feature values respectively. The
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6.2.2 Linguistic Lyric Features In
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mixed with regard to its usefulness
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Table 6.2 Text stylistic features e
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6.3 IMPLEMENTATION The Snowball ste
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shows the best combined feature set
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6.4.3 Analysis of Text Stylistic Fe
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Figure 6.1 Distributions of “!”
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stylistic features appeared to be a
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Support Vector Machines (SVM) as it
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then compared to the feature concat
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According to the average accuracies
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systems and the audio-only system w
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Table 7.4 Accuracies of lyric and a
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7.4.2 Top-Ranked Features Based on
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Again, these top-ranked features se
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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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