improving music mood classification using lyrics, audio and social tags

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music mood classification which involves dozens of mood categories. Previous experiments on automatic music mood classification usually considered only a handful of mood categories which likely simplified the real problem. However, in music mood classification, when the number of categories gets bigger than 10, the performances of multi-class classification algorithms become very low and lose their practical value (Li & Ogihara, 2004). Second, multi-class classification is usually adopted for experiments where the number of instances in each category is equal. However, in order to maximize the usage of the available audio and lyrics data, the experiment dataset in this dissertation research contains different number of instances in each category (see Section 5.2). 4.1.2 Performance Measure and Statistical Test Commonly used performance measures for classification problems include accuracy, precision, recall and F-measure. Table 4.1 shows a contingency table of a binary prediction. Compared to the ground truth, a prediction can be the following: true positive (TP): the prediction and truth are both positive; false negative (FN): the prediction is negative but the truth is positive; false positive (FP): the prediction is positive but the truth is negative; and true negative (TN): the prediction and truth are both negative. Table 4.1 Contingency table of binary classification results Ground truth Prediction Positive Negative Positive True positive False negative Negative False positive True negative The performance measures are defined as follows: 47
accuracy = TP + TN TP + FN + FP + TN ; precision = TP ; recall = TP + FP TP TP + FN F β = 2 ( β + 1) * precision * recall the importance of recall , β ≥ 0, whereβ = . 2 β * precision + recall the importance of precision Usually β = 1 giving equal importance to precision and recall: F 1 = 2 * precision * recall . precision + recall Accuracy has been extensively adopted in binary classification evaluations in text categorization. In MIR, especially MIREX, accuracy has been commonly reported in evaluating classification tasks. Therefore, accuracy will be used as the classification performance measure in this dissertation research. In evaluations of multiple categories, a concise and reliable measure of average performance is desirable. There are two approaches to calculating the average performance over all categories: micro-average and macro-average. Micro-average first gets the sums for all four cells in the contingency table (Table 4.1) across categories before calculating the final performance measure using the above formulas, while macro-average calculates the performance measures for each category and then takes the mean as the final score. Micro-averaging gives equal weight to each instance and therefore tends to be dominated by the classifier’s performance on big categories. Macro-averaging gives equal weight to each category, regardless of its size. Thus the two measures may give very different scores. This dissertation research puts equal emphasis on each mood category and thus macro-averaged measures are adopted for evaluation and comparison. In terms of splitting data into training and testing sets, both multiple randomized hold out tests and cross validation are often used in MIR classification evaluations. In a hold-out test the 48
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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
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: CHAPTER 4: CLASSIFICATION EXPERIMEN
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
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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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