Deep learning has recently empowered and democratized generative modeling of images and text, with additional concurrent works exploring the possibility of generating more complex forms of data, such as audio. However, the high dimensionality, long-range dependencies, and lack of standardized datasets currently makes generative modeling of audio and music very challenging. We propose to model music as a series of discrete notes upon which we can use autoregressive natural language processing techniques for successful generative modeling. While previous works used similar pipelines on data such as sheet music and MIDI, we aim to extend such approaches to the under-studied medium of guitar tablature. Specifically, we develop the first work to our knowledge that models one specific genre as guitar tablature: heavy rock. Unlike other works in guitar tablature generation, we have a freely available public demo at https://huggingface.co/spaces/josuelmet/Metal_Music_Interpolator
翻译:深层学习最近赋予了图像和文字的赋权和民主化基因模型,最近还同时开展了更多的工作,探索产生更复杂数据形式的可能性,例如音频。然而,由于高度的维度、远距离依赖性和缺乏标准化的数据集,目前音频和音乐的基因模型非常具有挑战性。我们提议将音乐模型作为一系列离散的笔记,我们可以使用自动递减性自然语言处理技术来成功地进行基因模型模型。虽然以前的工作在诸如床单音乐和MIDI等数据上使用了类似的管道,但我们的目标是将这类方法推广到研究不足的吉他标签介质媒介。具体地说,我们开发了第一件工作,使我们知道将一种特定基因模型作为吉他标签:重岩石。不同于吉他标签生成的其他作品,我们在https://huggingface.co/spaces/josuelmet/Metal_OICAtors。我们可以在https://huggingface.co/spaces/joceal/joelmelmet/Metal_OICAtor 上自由公开演示。