Text Data Analysis in Chinese Folk Music with Effective Clustering Model toward Feature Identification of Inheritance

Folk music based on big data analysis can provide valuable insights into the history, culture, and evolution of traditional music. By understanding the historical and cultural contexts of folk music, one better appreciate its value and contribute to its continued development and inheritance. Big data analysis can help identify patterns and trends in the performance, distribution, and reception of folk music across time and space. In this paper designed a Weighted Clustering Euclidean Feature (WCEF) model to evaluate folk music on the development of inheritance. Initially, the text data is extracted from folk music for the estimation of features in the big data analysis. Secondly, the WCEF model uses a clustering model for a subset of the folk music dataset with Weighted Non-Negative Matrix Factorization (WNMF). With the clustered model feature extraction is computed with Named Entity Recognition (NER). The NER model uses the Euclidean distance estimation for the computation of features in the folk data analysis. Finally, the WCEF model uses the deep learning model for the classification of inheritance in folk music. The experimental analysis stated that the WCEF model effectively classifies the folk music words and their contribution to inheritance

The Pitch Histogram of Traditional Chinese Anhemitonic Pentatonic Folk Songs

Funding Information: The APC was funded by Open Access Initiative of the University of Bremen and the DFG via SuUB Bremen. Publisher Copyright: © 2022 by the authors.As an essential subset of Chinese music, traditional Chinese folk songs frequently apply the anhemitonic pentatonic scale. In music education and demonstration, the Chinese anhemitonic pentatonic mode is usually introduced theoretically, supplemented by music appreciation, and a non-Chinese-speaking audience often lacks a perceptual understanding. We discovered that traditional Chinese anhemitonic pentatonic folk songs could be identified intuitively according to their distinctive bell-shaped pitch distribution in different types of pitch histograms, reflecting the Chinese characteristics of Zhongyong (the doctrine of the mean). Applying pitch distribution to the demonstration of the Chinese anhemitonic pentatonic folk songs, exemplified by a considerable number of instances, allows the audience to understand the culture behind the music from a new perspective by creating an auditory and visual association. We have also made preliminary attempts to feature and model the observations and implemented pilot classifiers to provide references for machine learning in music information retrieval (MIR). To the best of our knowledge, this article is the first MIR study to use various pitch histograms on traditional Chinese anhemitonic pentatonic folk songs, demonstrating that, based on cultural understanding, lightweight statistical approaches can progress cultural diversity in music education, computational musicology, and MIR.publishersversionpublishe

Spartan Daily, April 21, 1994

Volume 102, Issue 52https://scholarworks.sjsu.edu/spartandaily/8552/thumbnail.jp

Dance, Culture and Nationalism: the Socio-cultural Significance of Cloud Gate Dance Theatre in Taiwanese Society

The socio-cultural significance of Cloud Gate Dance Theatre (est. 1973) in Taiwan is manifested in the interconnection of political nationalism and the representation of a diasporic postcolonialist cultural nationalism in its dance creations. The hybrid nature of Taiwanese society and its struggle between Chinese and Taiwanese nationalism are reflected in the motive behind the creation of the company, the evolution of its repertoire and changes in its nationalist stance. The creation of Cloud Gate, the first Taiwanese contemporary dance company, was stimulated by its founder Lin Hwaimin's enthusiasm for Taiwan Chinese nationalism. The name Cloud Gate Dance Theatre not only relates to Chinese dance history and the formation of Chinese mythological nationalism, but also indicates the hybrid nature of Taiwanese society. In brief, Cloud Gate's multi-cultural dance creation is generated by diasporic Chinese for diasporic Chinese. In the light of intensifying Taiwanese nationalism on the island the evolution of the Cloud Gate repertoire (between 1973-1997), which began by juxtaposing Chinese and Western dance elements before integrating Chinese, Western, Taiwanese, Taiwanese indigenous and various Asian dance elements, reflects the company and Taiwanese society's search for a Taiwanese cultural and political identity. Among the Cloud Gate repertoire, Legacy (1978) and Nine Songs (1993) are considered to exemplify most this distinct socio-cultural phenomenon-the interaction and interconnection between dance, culture and nationalism in the context of the formation of Taiwan as a postcolonial society in opposition to Chinese nationalist hegemony. A research methodology for the socio-cultural analysis of dance is developed, with specific relevance to the Cloud Gate repertoire, which incorporates methods originating in sociology of dance and choreological studies. This is supported by a documentary research method which draws on theories and analytical methods of sociology and dance history. Zelinger's (1979) theory of semiotics of theatre dance is applied to bring together sociological and choreological methods. The examination of Thomas' (1986) sociological analysis of dance, Adshead's (1988) and Sanchez-Colberg's (1992) dance structural analysis leads to the development of a new method of analysis. Geertz's (1973) concept of `Thick description' provides the theoretical ground for the interpretation of data collected through the analysis of extrinsic and intrinsic features of cultural phenomena. Consequently the significance of the dance in question can be addressed in terms of the complex network of interpretations of it within its socio-cultural context

The Urban Culture of Chinese Society in Bangkok: Cinemas, Broadcast and Literature, 1950S-1970S

Ph.DDOCTOR OF PHILOSOPH

Neural ProbabilisticModels for Melody Prediction, Sequence Labelling and Classification

Data-driven sequence models have long played a role in the analysis and generation of musical information. Such models are of interest in computational musicology, computer-aided music composition, and tools for music education among other applications. This dissertation beginswith an experiment tomodel sequences of musical pitch in melodies with a class of purely data-driven predictive models collectively known as Connectionist models. It was demonstrated that a set of six such models could performon par with, or better than state-of-the-art n-gram models previously evaluated in an identical setting. A new model known as the Recurrent Temporal Discriminative Restricted Boltzmann Machine (RTDRBM), was introduced in the process and found to outperform the rest of the models. A generalisation of this modelling task was also explored, and involved extending the set of musical features used as input by the models while still predicting pitch as before. The improvement in predictive performance which resulted from adding these new input features is encouraging for future work in this direction. Based on the above success of the RTDRBM, its application was extended to a non-musical sequence labelling task, namely Optical Character Recognition. This extension involved a modification to the model’s original prediction algorithm as a result of relaxing an assumption specific to the melody modelling task. The generalised model was evaluated on a benchmark dataset and compared against a set of 8 baseline models where it faired better than all of them. Furthermore, a theoretical extension to an existingmodel which was also employed in the above pitch prediction task - the Discriminative Restricted Boltzmann Machine (DRBM) - was proposed. This led to three new variants of the DRBM (which originally contained Logistic Sigmoid hidden layer activations), withHyperbolic Tangent, Binomial and Rectified Linear hidden layer activations respectively. The first two of these have been evaluated here on the benchmark MNIST dataset and shown to perform on par with the original DRBM