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Abstract In recent years, Artificial Intelligence Generated Content (AIGC) technologies have advanced rapidly, with models such as Stable Diffusion and GPT garnering significant attention across various domains. Against this backdrop, AI-driven music composition techniques have also produced significant progress. However, no existing model has yet demonstrated the capability to generate Chinese-style music corresponding to Chinese-style videos. To address this gap, this study proposes a novel Chinese-style video music generation model based on the Latent Diffusion Model (LDM) and Diffusion Transformers (DiT). Experimental results demonstrate that the proposed model generates Chinese-style music from Chinese-style videos and achieves performance comparable to the baseline models in audio quality, distribution fitting, musicality, rhythmic stability, and audio-visual synchronization. These findings indicate that the model captures the stylistic features of Chinese music. This research not only demonstrates the feasibility applications of artificial intelligence in music creation but also provides a new technological approach to preserve and innovate the traditional Chinese music culture in the digital era. Furthermore, it explores new possibilities for the dissemination and innovation of Chinese cultural arts in the digital age.

In today's world, the rapid development of Chinese culture and its influence on the music scene underscore the necessity for systematic research into the Chinese piano chamber ensemble. The growing interest in this musical genre challenges many widely accepted notions about musical tradition and prompts a deeper understanding of its characteristics and impact on contemporary music art. The purpose of the research is to study and analyse the musical and cultural interrelationships within the Chinese piano chamber ensemble to better understand its essence and role in the modern music world. The research methodology is based on a comprehensive approach, incorporating analysis of musical material, historical and cultural methods, as well as comparative analysis. Modern theoretical concepts are applied to reveal the significance of the piano chamber ensemble in the context of cultural transformation and to examine the interaction and influence of national and non-national musical traditions on the formation of the musical genre, as well as its role in cultural exchange and interaction between different cultural traditions. The scientific novelty of the research lies in the first attempt to systematise and analyse the musical-cultural interrelationships within the Chinese piano chamber ensemble, which opens up new horizons for further understanding of musical culture and processes of cultural exchange in the contemporary world. The conclusions of the research testify to the importance of the Chinese piano chamber ensemble in contemporary music culture and its potential as a phenomenon of musical art. It not only reflects the interrelationship between national and non-national musical traditions but also serves as a significant factor in cultural exchange and interaction between different cultural traditions. [ABSTRACT FROM AUTHOR]

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In the context of rapid advancements in artificial intelligence technology, AI-powered music composition has demonstrated remarkable creative capabilities. However, no existing music generation model has been able to produce authentic waveform-level traditional Chinese music. To explore the potential of this field and address the limitations of current technologies in generating traditional Chinese music, this study introduces CPTGZ (Chinese Painting to Guzheng Music), a music generation model based on latent diffusion and Transformer architectures. CPTGZ aims to achieve automatic generation of waveform-level Guzheng music from Chinese paintings, thereby addressing the inability of existing music generation models to produce traditional Chinese music.To support the development and training of the model, we constructed a large-scale dataset of paired Chinese paintings and Guzheng music, consisting of 22,103 sample pairs. Through experimental evaluation, we found that CPTGZ exhibits excellent performance in terms of music quality and Guzheng-specific characteristics. The results demonstrate that our model can generate Chinese Guzheng music pieces highly correlated in style and semantics with the input Chinese paintings. Furthermore, the musical qualities of the generated Guzheng compositions demonstrate the characteristics of traditional Chinese music, thus validating the feasibility and effectiveness of our model.This research contributes to the field of AI-driven music generation by addressing the specific challenges of creating authentic traditional Chinese music, particularly Guzheng compositions, based on visual art inputs. The successful implementation of CPTGZ not only opens new avenues for cross-modal generation in the domain of culturally specific art forms, but also demonstrates the potential for AI to preserve and innovate within traditional art forms.

This study aims to investigate the dissemination of traditional Chinese music in the digital era and the application of deep learning in predicting the influence of Weibo content propagation. Firstly, the impact of digitization on the dissemination of traditional Chinese music is analyzed. Secondly, based on the Bidirectional Gated Recurrent Unit (Bi-GRU), traditional content feature modeling methods are optimized by introducing content text features. Simultaneously, the Graph Attention Network (GAN) is divided into three steps, allowing it to consider the edge properties of input sequences. The improved content feature modeling, GAN, and multilayer perceptron are integrated to construct a Context-dependent Dynamic Graph Attention Network (C-DGAN). In order to validate the performance of the C-DGAN model, Mean Square Logarithmic Error (MSLE) is used as the evaluation metric in comparative experiments at observation times T=1, 2, 3, and 4 hours. The results indicate that at T=4 hours, C-DGAN achieves an MSLE of 1.854, reducing by at least 0.134 compared to the baseline model, demonstrating superior performance in predicting the scale of Weibo content propagation. Additionally, in comparison with models using different recurrent neural networks, the model employing the Bi-GRU network performs the best. Thus, the proposed C-DGAN model exhibits excellent performance in predicting Weibo content propagation influence. The study findings provide robust support for the study and practice of Weibo content propagation.

The main objective of this article is to investigate the effectiveness of utilizing a supplementary learning model through Massive Open Online Courses (MOOCs) to reform traditional Chinese music education in higher education institutions in China. The study involved 314 fourth-year students from three higher education institutions, with an average age of 22.22 years (SD = 0.45). Two self-report scales were employed as instruments. The research demonstrated a significant improvement in the assimilation of traditional Chinese music among students who integrated MOOCs into their coursework, with an increase of 8.2 points. In the experimental group, all key indicators significantly increased: student satisfaction rose by 8.4 points, academic performance increased by 6.8 points, and interest in further study increased by 9.25 points. The enhancement of all examined indicators in the experimental group indicates the positive impact of MOOCs on musical education and cultural capital, confirming the effectiveness of a synergistic approach. Thus, the research results underscore the potential of online courses in enriching the educational process and expanding access to cultural heritage. The scholarly value of this research is evident in confirming the effectiveness of integrating MOOCs to enhance the study of traditional Chinese music, serving as a foundation for further development of educational methodologies and technologies. The practical significance lies in the potential application of the results to improve the quality of musical education in higher education institutions and broaden global access to cultural heritage. [ABSTRACT FROM AUTHOR]

The purpose of the article is to highlight the issue and development of Chinese traditional music in the context of modern society. Our research methodology is to justify and analyse the possibilities of immersive approaches to this important musical heritage. The research methodology is based on a comprehensive analysis of modern trends in the development of Chinese traditional music. Cultural and musicological methods were used to study the influence of modern society on the preservation of traditional musical culture. The study considers three inversion approaches: creation of exhibition halls, formation of archives, and development of ecological exhibition spaces. We conduct a comparative analysis of the effectiveness of each approach and its use for the preservation and development of traditional music. Scientific novelty is the systematic contribution of inversion methods to the preservation and development of traditional music. The approach takes into account modern realities and these demands of society, contributing not only to the preservation, but also to the active development of Chinese traditional musical heritage. The conclusions emphasise the expediency and necessity of implementing such research approaches and indicate the prospects of using immersive approaches for the further preservation and development of Chinese traditional music in the modern era. [ABSTRACT FROM AUTHOR]

The author, in the text below, discusses her 4 choreographies based on Chinese culture and art. There are two compositions by Pierre - Octave Ferrous from Trois Pieces pour flûte seule series , another two represent the music of Chinese composers: Zhang Qu - Musical Letters and Huang Haihuai – Horse Racing. Shengya Huang in her artistic works used the elements of traditional Chinese dance and Chinese props. The premiere of the choreographies described below took place in Poland, in 2023, and was performed by the students of The Eurhythmics Faculty at Stanislaw Moniuszko Music Academy of Gdańsk and by the author herself. She presented her artistic works in China, as well, with the participation of The College Music and Dance, Nanchang Vocational University students .

Generating compact and effective numerical representations of data is a fundamental step for many machine learning tasks. Traditionally, handcrafted features are used but as deep learning starts to show its potential, using deep learning models to extract compact representations becomes a new trend. Among them, adopting vectors from the model’s latent space is the most popular. There are several studies focused on visual analysis of latent space in NLP and computer vision. However, relatively little work has been done for music information retrieval (MIR) especially incorporating visualization. To bridge this gap, we propose a visual analysis system utilizing Autoencoders to facilitate analysis and exploration of traditional Chinese music. Due to the lack of proper traditional Chinese music data, we construct a labeled dataset from a collection of pre-recorded audios and then convert them into spectrograms. Our system takes music features learned from two deep learning models (a fully-connected Autoencoder and a Long Short-Term Memory (LSTM) Autoencoder) as input. Through interactive selection, similarity calculation, clustering and listening, we show that the latent representations of the encoded data allow our system to identify essential music elements, which lay the foundation for further analysis and retrieval of Chinese music in the future.