Towards a Deep Improviser: a prototype deep learning post-tonal free music generator

Dean, Roger T.; Forth, Jamie

doi:10.1007/s00521-018-3765-x

Cited by 7 publications

(4 citation statements)

References 31 publications

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“…As an important part of music information retrieval, music emotion classification based on audio has attracted more and more attention. The second-level emotional expression in national vocal music mainly refers to the singer's realization of the emotional state through deeper excavation, such as through sound, expression, and movement form [15]. The article builds the model shown in Fig.…”

Section: Analysis and Implementation Of National Music Deep Learning ...mentioning

confidence: 99%

Application Model Construction of Emotional Expression and Propagation Path of Deep Learning in National Vocal Music

Tang

2023

IJACSA

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Emotional expression is important in Chinese national vocal music art. The emotional expression in national vocal music is based on the art of national vocal music, with distinct characteristics and requirements. The ultimate goal is to spread the expression of various emotions in the national vocal music art. Promoting the spread of national vocal music singing art using modern media is an urgent requirement for the inheritance and development of national vocal music singing art. With the rapid development of science and technology, integrating deep learning and traditional music has become the general trend. It has been gradually applied to melody recognition, intelligent composition, virtual performance, and other aspects of traditional music and has achieved good results, but also hidden behind a series of ideas and technical and ethical issues. In this paper, the application of deep learning has been discussed and prospected. The recognition rate of emotional expression in national vocal music is 92 %. In terms of communication, combined with the deep learning algorithm, this paper analyzes the characteristics and requirements of emotional expression in the art of national vocal music singing and puts forward a new method of promoting the development of the art of national vocal music singing, hoping to attract more attention and enhance the social awareness of the application field, to promote the steady development of Chinese traditional music in the information age.

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Section: Analysis and Implementation Of National Music Deep Learning ...mentioning

confidence: 99%

Application Model Construction of Emotional Expression and Propagation Path of Deep Learning in National Vocal Music

Tang

2023

IJACSA

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“…Briot et al (2017) built a value music style automatic learning and generation model based on the deep learning, and found that deep learning algorithms can automatically generate music without human interaction, but there were still some problems of control, creativity, and interaction [17]. Dean et al (2018) applied deep learning to generate music, and found that the synthesized music was roughly the same as the example [18]. Zhu et al (2020) constructed a multiinstrument collaborative arrangement model by using the deep learning and multi-task learning methods, and verified the superiority and effectiveness of the model on real data sets [19].…”

Section: A Application Progress Of Deep Learning In Arrangementmentioning

confidence: 99%

Usage of Deep Learning and Blockchain in Compilation and Copyright Protection of Digital Music

Cai

2020

IEEE Access

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In order to explore the application of deep learning algorithms in arrangement and composition, and the role of blockchain in the protection of digital music copyright, a monophonic melody composition model based on the deep generative adversarial networks (DCGANs) is constructed firstly, and the composition performance of the model is analyzed using hymn as input sample in this study. Later, the multi-instrument co-arrangement (MICA) model based on the multi-task learning is proposed, and the composition performance is analyzed by taking the actual music as an input sample. Finally, the improved practical byzantine fault tolerance (IPBFT) algorithm is proposed, and a digital music copyright protection system is designed based on the blockchain in this study. The results indicate that the accuracies constructed DCGANs model in predicting the Soprano and Alto voice melody are higher than those of the DeepBatch model by 2.29% and 3.32%, respectively. The performance on the harmony score, note accuracy, levenshtein similarity (LS), notes distribution mean square error, and empty as well as the convergence speed of the constructed MICA model are better than those of other models. The average transaction per second (TPS) value of the proposed IPBFT algorithm in the real digital music copyright protection system is 3469, which is superior to other blockchain technologies. Finally, the digital music copyright protection system is achieved, the error rate of completing the request is 0% in the state of many users operating concurrently, and a high TPS value can be guaranteed. In short, the DCGANs and MICA models pointed out in this study can be used in the composition of monophonic melodies and complex melodies, and the digital music copyright protection system based on the blockchain has excellent performance in practical applications.

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“…Recently, deep learning [20][21][22][23] has shown a remarkable performance in extraction and expression of the effective features and inherent mapping relationships in high-dimensional state space. Compared with the traditional neural network, deep learning can extract the inherent features between adjacent states, learn inherent mapping relationships of the system state-action space, and describe the nonlinear process well.…”

Section: Introductionmentioning

confidence: 99%

Deep representation-based packetized predictive compensation for networked nonlinear systems

Chen

Cao

Kang

et al. 2020

Neural Comput & Applic

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The design of networked nonlinear control system is a very challenging problem due to the coupling of the system uncertainties (e.g., model accuracy, noise, nonlinearity) and network effects (e.g., packet dropouts, time delay). In this paper, a deep representation-based predictive compensation method is proposed for networked nonlinear systems with packet-dropouts in the feedback and forward channel. Different from the existing compensation methods based on openloop prediction, the proposed method is based on feedback compensation and does not require a nominal system model, so that it can avoid the coupling of system uncertainties and network effects as well as the occupation of network bandwidth. Specifically, a deep sequence to sequence learning scheme is firstly employed to encode the correlations of state and control sequences into deep feature representations. Furthermore, within the embedding space spanned by the learned features, according to the state-control sequence of each sampling step, a prediction of the next control command is generated as compensation for packet dropout. The stability of the overall system is rigorously proved by the Lyapunov theory, which reveals that the control errors for the networked control systems with packet dropouts asymptotic converge to a small neighborhood of the origin. We further evaluate the performance of the proposed strategy on a wheeled mobile robots simulation platform, and the experimental results demonstrate that our method can achieve high compensation accuracy and robustness concerning packet dropouts, even in the case of the maximum continuous packet dropouts specified by the network communication protocol.

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Towards a Deep Improviser: a prototype deep learning post-tonal free music generator

Cited by 7 publications

References 31 publications

Application Model Construction of Emotional Expression and Propagation Path of Deep Learning in National Vocal Music

Application Model Construction of Emotional Expression and Propagation Path of Deep Learning in National Vocal Music

Usage of Deep Learning and Blockchain in Compilation and Copyright Protection of Digital Music

Deep representation-based packetized predictive compensation for networked nonlinear systems

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