improving music mood classification using lyrics, audio and social tags

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Timbral features are musical surface features based on signal spectrum obtained by a Short Time Fourier Transformation (McEnnis, McKay, Fujinaga, & Depalle, 2005). The most popular ones are: 1) Spectral Centroid: mean of the amplitudes of the spectrum. It indicates the “brightness” of a musical signal. 2) Spectral Rolloff: the frequency where 85% of the energy in the spectrum is below this point. It is an indicator of the skewness of the frequencies in a musical signal. 3) Spectral Flux: spectral correlation between adjacent time windows. It is often used as an indication of the degree of change of the spectrum between windows. 4) Average Silence Ratio (also called Low Energy Rate): the percentage of frames with a less than average energy. 5) MFCCs: stands for Mel Frequency Cepstral Coefficients, the dominant features used for speech recognition. The mel-frequency cepstrum (MFC) is a representation of the shortterm power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency. The MFC has been proven to approximate the human auditory system’s response more closely than the normal cepstrum, and MFCCs are coefficients that collectively make up an MFC. Rhythmic features represent the beat and tempo of the musical piece. Those can be useful in music mood classification. Intuitively, fast music tends to be exciting rather than relaxing. The most frequently used rhythmic features include: 25
1) Tempo: also called “rhythmic periodicity,” is estimated from both a spectral analysis of each band of the spectrogram and the assessment of autocorrelation in the amplitude envelope extracted from the audio. 2) Beat Histogram: a histogram representing distribution of tempi in a musical excerpt. Usually the most prominent peak corresponds to the best tempo match. Experiments often use several properties of the beat histogram such as the bpm (beats per minute) values of the two highest peaks and the sum of all histogram bins, etc. 2.3.2 Text-based Music Mood Classification Very recently, several studies on music mood classification have been conducted using only music lyrics (He et al., 2008; Hu, Chen, & Yang, 2009b). He et al. (2008) compared traditional bag-of-words features in unigrams, bigrams, trigrams and their combinations, as well as three feature representation models (i.e., Boolean, absolute term frequency and tfidf weighting). Their results showed that the combination of unigram, bigram and trigram tokens with tfidf weighting performed the best, indicating that higher-order bag-of-words features captured more semantics useful for mood classification. Hu et al. (2009b) moved beyond the bag-of-words lyric features and extracted features based on an affective lexicon translated from the Affective Norms for English Words (ANEW) (Bradley & Lang, 1999). The datasets used in both studies were relatively small: the dataset in He et al. (2008) contained 1,903 songs in only two mood categories, “love” and “lovelorn,” while Hu et al. (2009b) classified 500 Chinese songs into four mood categories derived from Russell’s arousal-valence model. From a different angle, Bischoff, Firan, Nejdl, and Paiu (2009a) tried to use social tags to predict mood and theme labels of popular songs. The authors designed the experiments as a tag 26
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: environment by real-life users. The
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
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6.3 IMPLEMENTATION The Snowball ste
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shows the best combined feature set
Page 95 and 96:
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
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
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Thayer, R. E. (1989). The Biopsycho
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18. “music fade” at end of a se
Page 139 and 140:
48. Segment annotation plus specifi
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