Discriminating Mood Taxonomy of Chinese Traditional Music and Western Classical Music with Content Feature Sets

Wu, Wen; Xie, Lingyun

doi:10.1109/cisp.2008.272

Cited by 11 publications

(4 citation statements)

References 6 publications

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“…“calm,” “sad”) on both websites. These findings seem to be in line with those of Wu and Xie (2008) and Yang and Hu (2012) that introverted emotions and emotion expressions with soft terms are more appreciated in Chinese culture than in Western cultures (McCrae et al , 1996).…”

Section: Resultssupporting

confidence: 87%

Mood metadata on Chinese music websites: an exploratory study with user feedback

Cheong

Zhang

et al. 2018

OIR

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Purpose Music mood is an important metadata type on online music repositories and stream music services worldwide. Many existing studies on mood metadata have focused on music websites and services in the Western world to the exclusion of those serving users in other cultures. The purpose of this paper is to bridge this gap by exploring mood labels on influential Chinese music websites. Design/methodology/approach Mood labels and the associated song titles were collected from six Chinese music websites, and analyzed in relation to mood models and findings in the literature. An online music listening test was conducted to solicit users’ feedback on the mood labels on two popular Chinese music websites. Mood label selections on 30 songs from 64 Chinese listeners were collected and compared to those given by the two websites. Findings Mood labels, although extensively employed on Chinese music websites, may be insufficient in meeting listeners’ needs. More mood labels of high arousal semantics are needed. Song languages and user familiarity to the songs show influence on users’ selection of mood labels given by the websites. Practical implications Suggestions are proposed for future development of mood metadata and mood-enabled user interfaces in the context of global online music access. Originality/value This paper provides insights on understanding the mood metadata on Chinese music websites and uniquely contributes to existing knowledge of culturally diversified music access.

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Section: Resultssupporting

confidence: 87%

Mood metadata on Chinese music websites: an exploratory study with user feedback

Cheong

Zhang

et al. 2018

OIR

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“…Few MER studies have compared Chinese and Western music. Wu and Xie experimented with Chinese and Western classical MER by collecting annotations of 20 Western classical music pieces and 20 Chinese classical music pieces and training a Bayesian network for MER [16]. The performance of Western classical MER is better than Chinese classical MER.…”

Section: Cross-cultural Music Emotion Studiesmentioning

confidence: 99%

A Comparative Study of Western and Chinese Classical Music Based on Soundscape Models

Fan

Yang

Dong

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Whether literally or suggestively, the concept of soundscape is alluded in both modern and ancient music. In this study, we examine whether we can analyze and compare Western and Chinese classical music based on soundscape models. We addressed this question through a comparative study. Specifically, corpora of Western classical music excerpts (WCMED) and Chinese classical music excerpts (CCMED) were curated and annotated with emotional valence and arousal through a crowdsourcing experiment. We used a sound event detection (SED) and soundscape emotion recognition (SER) models with transfer learning to predict the perceived emotion of WCMED and CCMED. The results show that both SER and SED models could be used to analyze Chinese and Western classical music. The fact that SER and SED work better on Chinese classical music emotion recognition provides evidence that certain similarities exist between Chinese classical music and soundscape recordings, which permits transferability between machine learning models.

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“…Using the method proposed in [16], we firstly label music with 4 categories (Anxious, Depression, Contentment, Exuberance). One piece of music may belong to multiple mood categories.…”

Section: Datasetmentioning

confidence: 99%

Towards a new reading experience via semantic fusion of text and music

Zhuang

et al. 2011

Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries

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In CADAL, there preserve a lot of Chinese classical literatures, including graceful prose and verse. These works written in ancient Chinese comparatively are concise in vocabulary and sentence patterns. But they express rich feelings and convey a wealth of information. Although can be explained in modern Chinese, the aesthetic sense in those works disappears. So we aim to illustrate the feeling in these works using Chinese traditional music which is also another part of Chinese culture. This is an interesting and challenging work. In this paper, the correlation between the text and music is studied. A novel approach is proposed to model the latent semantic association underlying the two medium. Based on the correlation model we learned from training data, we can associate a literary work (mainly verse and prose in our digital library) with a few music pieces automatically. When a reader is appreciating a literary work, a piece of background music is playing meanwhile, the information and emotion implied by the work and music blend together. The reader may be immersed into the emotion and obtain aesthetic enjoyment intensively. We implement the proposed method and design experiments to evaluate the performance of it. The experimental result substantiates the feasibility of the proposed approach in this paper.

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Discriminating Mood Taxonomy of Chinese Traditional Music and Western Classical Music with Content Feature Sets

Cited by 11 publications

References 6 publications

Mood metadata on Chinese music websites: an exploratory study with user feedback

Mood metadata on Chinese music websites: an exploratory study with user feedback

A Comparative Study of Western and Chinese Classical Music Based on Soundscape Models

Towards a new reading experience via semantic fusion of text and music

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