Menghasilkan Background Game Music dengan Menggunakan Deep Convolutional Generative Adversarial Network

Daniel Widjojo, Henry Novianus Palit, Alvin Nathaniel Tjondrowiguno

Abstract


One of many important factor in producing a good quality game is the existence of a good quality Background Game Music (BGM). But the difficulty of getting game music assets makes game developers have to pay extra money, waste more time and effort to get a good quality game music. This can hinder the process of making a game. For that reason, if there is a program that is able to produce BGM with good quality, it will greatly help the work of the game developer.
The method used in this study is the Deep Convolutional Generative Adversarial Network (DCGAN). The data that being used is Musical Instrument Digital Interface (MIDI) file format which will then be converted into a pianoroll format. The pianoroll will then be converted into a matrix and entered into the DCGAN model. Before conducting training process, it is necessary to make a preprocessing, postprocessing and a model DCGAN. Testing in this study is done by finding the best parameters and architecture of DCGAN to produce Background Game Music with good quality.
The test results show that DCGAN is a very sensitive model in terms of architecture and hyperparameter, therefore it needs extra attention in tuning architecture and hyperparameter for DCGAN. Besides that, music that is converted into pianoroll format lacks the ability to highlight its features and making it difficult to learn by DCGAN. From the end results, DCGAN is able to produce Background Game Music but with poor quality.

Keywords


Deep Convolutional Generative Adversarial Network; Music Generation; Tensorflow; Keras; Pianoroll; Background Game Music.

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References


Chintala, S., Denton, E., Arjovsky, M. & Mathieu, M., 2020. How to Train a GAN? Tips and tricks to Make GANs Work. (5 March 2020). URI= https://github.com/soumith/ganhacks.

Choi, K., Fazekas, G. & Sandler, M., 2016. Text-based LSTM Networks for Automatic Music Composition. pp. 1-8, 2016. DOI= https://arxiv.org/abs/1604.05358.

Dong, H.-W., Hsiao, W.-Y. & Yang, Y.-H., 2018. Pypianoroll: Open Source Python Package for Handling Multitrack Pianorolls. in Late-Breaking Demos of the 18th International Society for Music Information Retrieval Conference (ISMIR), pp. 1-2. pp. 1-2, 2018. DOI= https://salu133445.github.io/pypianoroll/

Dong, H.-W., Hsiao, W.-Y., Yang, L.-C. & Yang, Y.-H., 2018. MuseGAN: Multi-Track Sequential Generative. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, pp. 34-41, 2018. DOI= https://arxiv.org/abs/1709.06298

J. Hui, Hui, J., 2018. GAN — DCGAN (Deep convolutional generative adversarial networks), (18 Juny 2018). URI= https://medium.com/@jonathan_hui/gan-dcgan-deep-convolutional-generative-adversarial-networks-df855c438f.

Kotecha, N., 2018. Bach2Bach: Generating Music Using A Deep Reinforcement Learning Approach. 2018. DOI= ttps://arxiv.org/abs/1812.01060

Lane, T., 2018. 1D & 3D Convolutions explained with… MS Excel!. (17 October 2018). URI= https://medium.com/apache-mxnet/1d-3d-convolutions-explained-with-ms-excel-5f88c0f35941.

Nararya, H. R., 2019. Wawancara Bersama Maulidan Games. [Interview]. (25 November 2019).

Newman, M. et al., 2019. VGMusic - Game Music MIDI files. (16 September 2019). URI= http://vgmusic.com.

Radford , A., Metz, L. & Chintala, S., 2016. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings, pp. 1-16, 2016. DOI= https://arxiv.org/abs/1511.06434

Rogers, S., 2010. Level Up! The Guide to Great Video Game Design. Chichester: Wiley.

Wibisono, A., 2019. Wawancara Bersama Regulus Studio [Interview] (23 November 2019


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