Uneingeschränkter Zugang

A stochastic process model of melody generation in popular music composition and its contribution to compositional innovation

  
21. März 2025

Zitieren
COVER HERUNTERLADEN

Briot, J. P., & Pachet, F. (2020). Deep learning for music generation: challenges and directions. Neural Computing and Applications, 32(4), 981-993. Briot J. P. Pachet F. ( 2020 ). Deep learning for music generation: challenges and directions . Neural Computing and Applications , 32 ( 4 ), 981 - 993 . Search in Google Scholar

Herremans, D., Chuan, C. H., & Chew, E. (2017). A functional taxonomy of music generation systems. ACM Computing Surveys (CSUR), 50(5), 1-30. Herremans D. Chuan C. H. Chew E. ( 2017 ). A functional taxonomy of music generation systems . ACM Computing Surveys (CSUR) , 50 ( 5 ), 1 - 30 . Search in Google Scholar

Copet, J., Kreuk, F., Gat, I., Remez, T., Kant, D., Synnaeve, G., … & Défossez, A. (2024). Simple and controllable music generation. Advances in Neural Information Processing Systems, 36. Copet J. Kreuk F. Gat I. Remez T. Kant D. Synnaeve G. Défossez A. ( 2024 ). Simple and controllable music generation . Advances in Neural Information Processing Systems, 36 . Search in Google Scholar

Mao, H. H., Shin, T., & Cottrell, G. (2018, January). DeepJ: Style-specific music generation. In 2018 IEEE 12th International Conference on Semantic Computing (ICSC) (pp. 377-382). IEEE. Mao H. H. Shin T. Cottrell G. ( 2018 , January ). DeepJ: Style-specific music generation . In 2018 IEEE 12th International Conference on Semantic Computing (ICSC) (pp. 377 - 382 ). IEEE . Search in Google Scholar

Civit, M., Civit-Masot, J., Cuadrado, F., & Escalona, M. J. (2022). A systematic review of artificial intelligence-based music generation: Scope, applications, and future trends. Expert Systems with Applications, 209, 118190. Civit M. Civit-Masot J. Cuadrado F. Escalona M. J. ( 2022 ). A systematic review of artificial intelligence-based music generation: Scope, applications, and future trends . Expert Systems with Applications , 209 , 118190 . Search in Google Scholar

Ji, S., Yang, X., & Luo, J. (2023). A survey on deep learning for symbolic music generation: Representations, algorithms, evaluations, and challenges. ACM Computing Surveys, 56(1), 1-39. Ji S. Yang X. Luo J. ( 2023 ). A survey on deep learning for symbolic music generation: Representations, algorithms, evaluations, and challenges . ACM Computing Surveys , 56 ( 1 ), 1 - 39 . Search in Google Scholar

Yadav, P. S., Khan, S., Singh, Y. V., Garg, P., & Singh, R. S. (2022). A Lightweight Deep Learning-Based Approach for Jazz Music Generation in MIDI Format. Computational Intelligence and Neuroscience, 2022(1), 2140895. Yadav P. S. Khan S. Singh Y. V. Garg P. Singh R. S. ( 2022 ). A Lightweight Deep Learning-Based Approach for Jazz Music Generation in MIDI Format . Computational Intelligence and Neuroscience , 2022 ( 1 ), 2140895 . Search in Google Scholar

Min, J., Liu, Z., Wang, L., Li, D., Zhang, M., & Huang, Y. (2022). Music generation system for adversarial training based on deep learning. Processes, 10(12), 2515. Min J. Liu Z. Wang L. Li D. Zhang M. Huang Y. ( 2022 ). Music generation system for adversarial training based on deep learning . Processes , 10 ( 12 ), 2515 . Search in Google Scholar

Maduskar, A., Ladukar, A., Gore, S., & Patwari, N. (2020, February). Music generation using deep generative modelling. In 2020 International Conference on Convergence to Digital World-Quo Vadis (ICCDW) (pp. 1-4). IEEE. Maduskar A. Ladukar A. Gore S. Patwari N. ( 2020 , February ). Music generation using deep generative modelling . In 2020 International Conference on Convergence to Digital World-Quo Vadis (ICCDW) (pp. 1 - 4 ). IEEE . Search in Google Scholar

Pricop, T. C., & Iftene, A. (2024). Music Generation with Machine Learning and Deep Neural Networks. Procedia Computer Science, 246, 1855-1864. Pricop T. C. Iftene A. ( 2024 ). Music Generation with Machine Learning and Deep Neural Networks . Procedia Computer Science , 246 , 1855 - 1864 . Search in Google Scholar

Bhardwaj, S., Salim, S. M., Khan, T. A., & JavadiMasoudian, S. (2022, October). Automated Music Generation using Deep Learning. In 2022 International Conference Automatics and Informatics (ICAI) (pp. 193-198). IEEE. Bhardwaj S. Salim S. M. Khan T. A. JavadiMasoudian S. ( 2022 , October ). Automated Music Generation using Deep Learning . In 2022 International Conference Automatics and Informatics (ICAI) (pp. 193 - 198 ). IEEE . Search in Google Scholar

Mor, B., Garhwal, S., & Kumar, A. (2021). A systematic review of hidden Markov models and their applications. Archives of computational methods in engineering, 28, 1429-1448. Mor B. Garhwal S. Kumar A. ( 2021 ). A systematic review of hidden Markov models and their applications . Archives of computational methods in engineering , 28 , 1429 - 1448 . Search in Google Scholar

Franzese, M., & Iuliano, A. (2018). Hidden markov models. In Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics (Vol. 1, pp. 753-762). Elsevier. Franzese M. Iuliano A. ( 2018 ). Hidden markov models . In Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics (Vol. 1 , pp. 753 - 762 ). Elsevier . Search in Google Scholar

Grewal, J. K., Krzywinski, M., & Altman, N. (2019). Markov models—hidden Markov models. Nature methods, 16(9), 795-796. Grewal J. K. Krzywinski M. Altman N. ( 2019 ). Markov models—hidden Markov models . Nature methods , 16 ( 9 ), 795 - 796 . Search in Google Scholar

Awad, M., Khanna, R., Awad, M., & Khanna, R. (2015). Hidden markov model. Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, 81-104. Awad M. Khanna R. Awad M. Khanna R. ( 2015 ). Hidden markov model . Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers , 81 - 104 . Search in Google Scholar

Wang, K., Gou, C., Duan, Y., Lin, Y., Zheng, X., & Wang, F. Y. (2017). Generative adversarial networks: introduction and outlook. IEEE/CAA Journal of Automatica Sinica, 4(4), 588-598. Wang K. Gou C. Duan Y. Lin Y. Zheng X. Wang F. Y. ( 2017 ). Generative adversarial networks: introduction and outlook . IEEE/CAA Journal of Automatica Sinica , 4 ( 4 ), 588 - 598 . Search in Google Scholar

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … & Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139-144. Goodfellow I. Pouget-Abadie J. Mirza M. Xu B. Warde-Farley D. Ozair S. Bengio Y. ( 2020 ). Generative adversarial networks . Communications of the ACM , 63 ( 11 ), 139 - 144 . Search in Google Scholar

Gonog, L., & Zhou, Y. (2019, June). A review: generative adversarial networks. In 2019 14th IEEE conference on industrial electronics and applications (ICIEA) (pp. 505-510). IEEE. Gonog L. Zhou Y. ( 2019 , June ). A review: generative adversarial networks . In 2019 14th IEEE conference on industrial electronics and applications (ICIEA) (pp. 505 - 510 ). IEEE . Search in Google Scholar

Creswell, A., & Bharath, A. A. (2018). Inverting the generator of a generative adversarial network. IEEE transactions on neural networks and learning systems, 30(7), 1967-1974. Creswell A. Bharath A. A. ( 2018 ). Inverting the generator of a generative adversarial network . IEEE transactions on neural networks and learning systems , 30 ( 7 ), 1967 - 1974 . Search in Google Scholar

Wang, C., Xu, C., Yao, X., & Tao, D. (2019). Evolutionary generative adversarial networks. IEEE Transactions on Evolutionary Computation, 23(6), 921-934. Wang C. Xu C. Yao X. Tao D. ( 2019 ). Evolutionary generative adversarial networks . IEEE Transactions on Evolutionary Computation , 23 ( 6 ), 921 - 934 . Search in Google Scholar

Pinheiro Cinelli, L., Araújo Marins, M., Barros da Silva, E. A., & Lima Netto, S. (2021). Variational autoencoder. In Variational Methods for Machine Learning with Applications to Deep Networks (pp. 111-149). Cham: Springer International Publishing. Pinheiro Cinelli L. Araújo Marins M. Barros da Silva E. A. Lima Netto S. ( 2021 ). Variational autoencoder . In Variational Methods for Machine Learning with Applications to Deep Networks (pp. 111 - 149 ). Cham: Springer International Publishing . Search in Google Scholar

Kusner, M. J., Paige, B., & Hernández-Lobato, J. M. (2017, July). Grammar variational autoencoder. In International conference on machine learning (pp. 1945-1954). PMLR. Kusner M. J. Paige B. Hernández-Lobato J. M. ( 2017 , July ). Grammar variational autoencoder . In International conference on machine learning (pp. 1945 - 1954 ). PMLR . Search in Google Scholar

Hou, X., Shen, L., Sun, K., & Qiu, G. (2017, March). Deep feature consistent variational autoencoder. In 2017 IEEE winter conference on applications of computer vision (WACV) (pp. 1133-1141). IEEE. Hou X. Shen L. Sun K. Qiu G. ( 2017 , March ). Deep feature consistent variational autoencoder . In 2017 IEEE winter conference on applications of computer vision (WACV) (pp. 1133 - 1141 ). IEEE . Search in Google Scholar

Shao, H., Yao, S., Sun, D., Zhang, A., Liu, S., Liu, D., … & Abdelzaher, T. (2020, November). Controlvae: Controllable variational autoencoder. In International conference on machine learning (pp. 8655-8664). PMLR. Shao H. Yao S. Sun D. Zhang A. Liu S. Liu D. Abdelzaher T. ( 2020 , November ). Controlvae: Controllable variational autoencoder . In International conference on machine learning (pp. 8655 - 8664 ). PMLR . Search in Google Scholar

Alejandro Moreno Sanfélix, F. Consuelo Gragera Peña & Miguel A. Jaramillo Morán. (2024). Predictive Model of Pedestrian Crashes Using Markov Chains in the City of Badajoz. Sustainability(22), 10115-10115. Sanfélix Alejandro Moreno Peña F. Consuelo Gragera Miguel A. Jaramillo Morán ( 2024 ). Predictive Model of Pedestrian Crashes Using Markov Chains in the City of Badajoz . Sustainability ( 22 ), 10115 - 10115 . Search in Google Scholar

George Datseris & Joel Hobson.(2019). MIDI. jl: Simple and intuitive handling of MIDI data.. J. Open Source Software(35), 1166. Datseris George Hobson Joel ( 2019 ). MIDI. jl: Simple and intuitive handling of MIDI data .. J. Open Source Software ( 35 ), 1166 . Search in Google Scholar

Roger Thornton Dean & Marcus Thomas Pearce. (2016). Algorithmically-generated Corpora that use Serial Compositional Principles Can Contribute to the Modeling of Sequential Pitch Structure in Non-tonal Music. Empirical Musicology Review(1), 27-46. Dean Roger Thornton Pearce Marcus Thomas ( 2016 ). Algorithmically-generated Corpora that use Serial Compositional Principles Can Contribute to the Modeling of Sequential Pitch Structure in Non-tonal Music . Empirical Musicology Review ( 1 ), 27 - 46 . Search in Google Scholar

Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
1 Hefte pro Jahr
Fachgebiete der Zeitschrift:
Biologie, Biologie, andere, Mathematik, Angewandte Mathematik, Mathematik, Allgemeines, Physik, Physik, andere