improving music mood classification using lyrics, audio and social tags

More documents

Recommendations

Info

Information science is an interdisciplinary field. It often involves topics that have been traditionally studied in other fields. Borrowing findings from literatures in other fields is a very important research method in information science, but researchers need to pay attention to connecting theories in the literature to the reality and social context of the problems under investigation. This research also proposed a method of building ground truth dataset using social tags. The method is efficient and flexible. It does not require recruiting human assessors and thus does not suffer low cross assessor consistency, the exact bottleneck of building large ground truth dataset in MIR. The method is flexible in that it can be applied to any music data available to the researcher. To date, the ground truth dataset built in this research is the largest experimental dataset with audio, lyrics and social tags for music mood classification. This research evaluated a number of lyric text features in the task of music mood classification, including the basic, commonly used bag-of-words features, features based on psycholinguistic lexicons, and text stylistic features. The results revealed that the most useful lyric features were combinations of content words, certain linguistic features, and text stylistic features. A surprising finding was that the combination of ANEW scores and text stylistic features achieved the second best performance (with no significant difference from the best one) among all feature types and combinations with only 37 dimensions in this feature set (compared to 107,360 in the top performance feature set). In terms of averaged performance across categories, the lyric-only system outperformed a leading audio-only system on this task, although the performance difference was a bit shy from being statistically significant. On individual categories, the two information sources show 111
different strengths. Lyric-based systems seem to have an advantage on categories where words in lyrics have good connection to the categories such as “angry” and “romantic.” However, future work is needed to make a conclusive claim. In combining lyrics and music audio, late fusion (linear interpolation with equal weights to both classifiers) yielded the best performance, and its performance was more stable across mood categories than the other hybrid method, feature concatenation. Both hybrid systems significantly outperformed (at p < 0.05) the audio-only system which was a top ranked system on this task. The late fusion system improved the performance of the audio-only system by 9.6%, demonstrating the effectiveness of combining lyrics and audio. Experiments on learning curves discovered that complementing audio with lyrics could reduce the number of training examples required to achieve the same performance level as single-source-based systems. The audio-only system appeared to have reached its potential and stops improving performance when given 80% of all training examples. In contrast, the hybrid systems could continue to improve performances if more training examples become available. Combining lyrics and audio can also reduce the demand on the length of audio used by the classifier. Very short audio clips (as short as 5 seconds), when combined with complete lyrics, outperformed single-source-based systems using all available audio or lyrics. In summary, this research identified music mood categories that reflect the reality of the music listening environment, made advances in lyric affect analysis, improved the effectiveness and efficiency of automatic music mood classification, and thus helped make mood a practical metadata type of music and access point in music repositories. 112
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 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
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: CHAPTER 9: CONCLUSIONS AND FUTURE R
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

Create successful ePaper yourself

Delete template?

Save as template?