improving music mood classification using lyrics, audio and social tags

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entire labeled dataset is split into training and testing subsets, and an average performance can be evaluated with multiple randomized hold-out tests with the same train/test split ratio. Cross validation (CV) is a simple heuristic evaluation. In the setting of m-fold cross validation, a training set is randomly or strategically divided into m disjoint subsets (folds) of equal size. The classifier is trained m times, each time with a different fold held out as the testing set. An average performance on the m runs can be calculated and evaluated. m = 3,5,10 are popular choices in MIR studies. For example, the AMC task in MIREX 2007 adopted a 3-fold cross validation. This dissertation research uses 10-fold cross validation. In comparing system performances, Friedman’s ANOVA will be applied to determine whether there are significant differences between the systems considered in each research question. Friedman’s ANOVA is a non-parametric test which does not require normal distribution of the sample data, and accuracy data are rarely distributed normally (Downie, 2008). The samples used in the tests will be accuracies on individual mood categories, unless otherwise indicated. 4.2 CLASSIFICATION ALGORITHM AND IMPLEMENTATION 4.2.1 Supervised Learning and Support Vector Machines A number of supervised learning algorithms have been invented and extensively adopted in both automatic text categorization and music classification. Supervised learning is a technique that calculates a classification function or model from training data and then uses the function or model to classify new and unseen data. Common supervised learning algorithms include decision trees such as Quinlan’s ID3 and C4.5, K-Nearest Neighbors (KNN), Naïve Bayesian algorithm, 49
Support Vector Machines (SVM), etc. (Sebastiani, 2002). In text categorization evaluation studies, Naïve Bayes and SVM are almost always considered. Naïve Bayes often serves as a baseline, while SVM seems to have the top performances (Yu, 2008). In MIR, music classification studies (mostly on genre classification) often choose KNN and/or decision trees (C4.5) as baselines to be compared to SVM. Results in both existing music classification experiments and MIREX classification tasks have shown that the SVM generally, if not always, outperforms other algorithms (e.g., Hu et al., 2008a; Laurier et al., 2008). As this research needs to combine both sources of audio and text, SVM is chosen as the classification algorithm. By design, SVM is a binary classification algorithm. For multi-class classification problems, a number of SVM have to be learned and each of them predicts the membership of examples to one class. In order to reduce the chance of overfitting, SVM attempts to find the classification plane in between two classes and maximizes the margin to either class (Burges, 1998). The data instances on the margins are called support vectors, while other instances are considered not contributive to the classification. SVM classifies a new instance by deciding on which side of the plane the vector of the instance would fall. An SVM with a linear kernel means that there exists a straight line in a two-dimensional space that separates one class from another (Figure 4.2). For datasets that are not linearly separable, higher order kernels are used to project the data to a higher dimensional space where they become linearly separable. Finding the classification plane involves a complicated computation of quadratic programming, and thus SVM are more computationally expensive than Naïve Bayes classifiers. However, SVM are very robust with noisy examples, and they can 50
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IMPROVING MUSIC MOOD CLASSIFICATION
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concatenation and late fusion (line
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ACKNOWLEDGMENTS I would have never
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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: accuracy = TP + TN TP + FN + FP + T
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
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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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