improving music mood classification using lyrics, audio and social tags

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CHAPTER 5: BUILDING A DATASET WITH TERNARY INFORMATION The experiments described in Chapter 4 need to be conducted against a ground truth dataset with ternary information sources available: audio, lyrics and social tags. Audio and lyrics are used to build the classifiers, while social tags are used for giving ground truth labels to examples in the dataset. This chapter describes the process of collecting and preprocessing the data with ternary information sources, as well as the process of building the ground truth dataset with mood labels given by social tags. 5.1 DATA COLLECTION 5.1.1 Audio Data Audio is the most difficult to obtain among all the three information sources, due to intellectual property and copyright laws imposed on music materials. For this reason, data collection for this research started from audio data accessible to the author. The author is affiliated with the International Music Information Retrieval Systems Evaluation Laboratory (IMIRSEL) where this dissertation research is conducted. The IMIRSEL is the host of MIREX each year, and has accumulated multiple audio collections of significant sizes and diversity (see Table 5.1). The audio data in this dissertation research were selected from the IMIRSEL collections. 53
Table 5.1 Information of audio collections hosted in IMIRSEL Audio Sets Format Number Avg. length of of tracks tracks (second) Short description USPOP wav (stereo) 8,791 253.6 Pop music in US USCRAP wav (stereo) 3,993 243.5 Unpopular music in US American wav (stereo) 5,291 183.2 American music Classical wav (stereo) 9,750 242.4 Classical music Metal/Elect. wav (stereo) 290 311.8 Metal & Electronica music Magnatune mp3 4,648 253.9 Music released by Magnatune The Beatles wav (stereo) 180 163.8 12 CDs of The Beatles Latin wav (stereo) 3,227 221.6 Latin music Assorted Pop mp3 609 233.8 Pop music in US and Europe Some audio collections shown in Table 5.1 are not usable for this research. Electronica and Classical music usually do not have lyrics. While the Latin collection has lyrics in Spanish, this research is limited to investigating lyrics in English. In addition, after these audio collections were merged into a super collection, a number of songs were found to be duplicates. In many cases, the duplicates were different recordings of the same song. For example, the song “Help!” by The Beatles appeared in two albums: one was the album “Help!” released in 1965; the other was the album “1” released in 2000. In such cases, the recording with the latest release date was chosen because its sound quality was (sometimes much) better than the older ones. After eliminating duplicates, the number of songs in each audio collection is shown in Table 5.2. As the audio-based system to be evaluated in this research, Marsyas, takes .wav files as input, the mp3 tracks were converted to .wav files using the ffmpeg program 9 . All the audio tracks used in the experiments were converted into 44.1 kHz stereo format before audio features were extracted using Marsyas. 9 Available at http://www.ffmpeg.org/ 54
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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 .
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7.2.2 Best Hybrid Method ..........
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LIST OF TABLES Table 3.1 Mood categ
Page 13 and 14: music itself and creating the socia
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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: 4.3 SUMMARY This chapter described
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
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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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