{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/convolutional-generative-adversarial-networks","title":"Convolutional Generative Adversarial Networks with Binary Neurons for Polyphonic Music Generation","arxiv_id":"1804.09399","date":"2018-04-25","proceeding":null,"authors":["Hao-Wen Dong","Yi-Hsuan Yang"],"abstract":"It has been shown recently that deep convolutional generative adversarial\nnetworks (GANs) can learn to generate music in the form of piano-rolls, which\nrepresent music by binary-valued time-pitch matrices. However, existing models\ncan only generate real-valued piano-rolls and require further post-processing,\nsuch as hard thresholding (HT) or Bernoulli sampling (BS), to obtain the final\nbinary-valued results. In this paper, we study whether we can have a\nconvolutional GAN model that directly creates binary-valued piano-rolls by\nusing binary neurons. Specifically, we propose to append to the generator an\nadditional refiner network, which uses binary neurons at the output layer. The\nwhole network is trained in two stages. Firstly, the generator and the\ndiscriminator are pretrained. Then, the refiner network is trained along with\nthe discriminator to learn to binarize the real-valued piano-rolls the\npretrained generator creates. Experimental results show that using binary\nneurons instead of HT or BS indeed leads to better results in a number of\nobjective measures. Moreover, deterministic binary neurons perform better than\nstochastic ones in both objective measures and a subjective test. The source\ncode, training data and audio examples of the generated results can be found at\nhttps://salu133445.github.io/bmusegan/ .","url_abs":"http://arxiv.org/abs/1804.09399v3","url_pdf":"http://arxiv.org/pdf/1804.09399v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"convolutional-generative-adversarial-networks","repo_url":"https://github.com/salu133445/musegan","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"convolutional-generative-adversarial-networks","repo_url":"https://github.com/lucylow/Stochastic_SoundCloud","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"convolutional-generative-adversarial-networks","repo_url":"https://github.com/salu133445/bmusegan","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"music-generation","task_name":"Music Generation"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"wgan-gp","method_name":"WGAN GP"},{"method_slug":"wgan-gp-loss","method_name":"WGAN-GP Loss"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.09399","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.09399"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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