Literature survey of multi-track music generation model based on generative confrontation network in intelligent composition

Liu, Weiming

doi:10.1007/s11227-022-04914-5

Cited by 20 publications

(4 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The research results showed that the music snippets generated by the proposed model had better artistic aesthetics. In the end, 62.8% of the listeners had difficulty distinguishing between the generated melodies and real melodies, demonstrating the high authenticity and effectiveness of the model in music generation [10]. Wang et al proposed a Transformer-based multi-track music generative adversarial network, aimed at adhering to music rules to generate works with higher musicality.…”

Section: Related Workmentioning

confidence: 99%

“…The one-dimensional array calculation formulas for RewardNetM, RewardNetA, and RewardNetG are given by Eq. (10). In each of the three reward networks, the calculation process for extracting action values from the reward value array is shown in Eq.…”

Section: B Construction Of a Multi-ac Melodic Rhythm Generation Model...mentioning

confidence: 99%

“…In each of the three reward networks, the calculation process for extracting action values from the reward value array is shown in Eq. ( 11) [22][23]. In Eq.…”

Section: B Construction Of a Multi-ac Melodic Rhythm Generation Model...mentioning

confidence: 99%

See 2 more Smart Citations

Multi-Track Music Generation Based on the AC Algorithm and Global Value Return Network

Guo

2024

IJACSA

View full text Add to dashboard Cite

In the current field of deep learning and music information retrieval, automated music generation has become a hot research topic. This study addresses the issues of low clarity and musicality in current multi-track music generation by combining the Actor-Critic algorithm and the Global Value Return Network to create a novel multi-track music generation model. The study first utilizes the Actor-Critic algorithm to generate single-track music rhythm and melody models. Building upon this foundation, the study further optimizes the single-track models using the Global Value Return Network and proposes the multi-track music model. The results demonstrate that the harmonization accuracy of the final multi-track music generation model ranges from 0.90 to 0.98, with a maximum value of 0.98. Additionally, the audience satisfaction and expert satisfaction of the model are 0.96 and 0.97, respectively, indicating that the model has a high musical appreciation value. Overall, the multi-track music generation model designed in this study addresses the limitations of single-track music generation and produces more rhythmically diverse multi-track music.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: B Construction Of a Multi-ac Melodic Rhythm Generation Model...mentioning

confidence: 99%

See 1 more Smart Citation

Multi-Track Music Generation Based on the AC Algorithm and Global Value Return Network

Guo

2024

IJACSA

View full text Add to dashboard Cite

show abstract

“…In order to avoid the "global integration" of economics as a substitute for the "global integration" of culture and values, it has become an urgent need to enhance national self-confidence, national psychological cohesion, and the national spirit to strengthen the education of traditional music and culture and to promote the national outstanding culture [1][2]. The charm and elegance of Chinese culture need to be explored, and the excellent culture that it contains shows the world in a new way [3][4]. In the process of traditional culture inheritance, education is an extremely important way of transmitting knowledge.…”

Section: Introductionmentioning

confidence: 99%

Informatization Integration Strategy of Modern Vocal Music Teaching and Traditional Music Culture in Colleges and Universities in the Era of Artificial Intelligence

Zhang

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

This paper utilizes deep learning algorithms to informally integrate modern vocal music teaching with traditional music culture and extracts audio time-domain features and frequency-domain features through neural network self-learning. Secondly, a large number of music tracks are decomposed into music patterns, which constitute a music pattern library, and a music training model is generated through the automatic music audio synthesis algorithm based on a recurrent neural network, and the GRU model is used for music training and model prediction. The strategy of integrating artificial intelligence and modern vocal music teaching mode through traditional music culture in modern vocal music teaching is informatized, and a controlled experiment is carried out with H Music Academy as an example. The results show that the average degree of completion of the learning objectives of the students in the two experimental classes is 89.32 and 87.16, respectively, which is 14.15 and 11.99 higher than the average degree of completion of the control class. This study demonstrates that the teaching mode of traditional music culture integration in modern vocal music teaching can enhance the student’s ability of vocal music skills and practically improve the students’ artistic literacy, which can improve the degree of completion of the student’s learning objectives and in turn, improve the overall level of vocal music teaching.

show abstract

Motifs, Phrases, and Beyond: The Modelling of Structure in Symbolic Music Generation

Bhandari,

Colton

2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Literature survey of multi-track music generation model based on generative confrontation network in intelligent composition

Cited by 20 publications

References 39 publications

Multi-Track Music Generation Based on the AC Algorithm and Global Value Return Network

Multi-Track Music Generation Based on the AC Algorithm and Global Value Return Network

Informatization Integration Strategy of Modern Vocal Music Teaching and Traditional Music Culture in Colleges and Universities in the Era of Artificial Intelligence

Motifs, Phrases, and Beyond: The Modelling of Structure in Symbolic Music Generation

Contact Info

Product

Resources

About