Department of Computer Science and Engineering, Mahamaya Polytechnic of Information Technology (Govt.), Hathras, Uttar Pradesh 204102, India.
Department of Computer Science & Engineering, Sunder Deep Engineering College, Ghaziabad 201002, Uttar Pradesh, India.
Comput Intell Neurosci. 2022 Aug 5;2022:2140895. doi: 10.1155/2022/2140895. eCollection 2022.
In today's real-world, estimation of the level of difficulty of the musical is part of very meaningful musical learning. A musical learner cannot learn without a defined precise estimation. This problem is not very basic but it is complicated up to some extent because of the subjectivity of the contents and the scarcity of the data. In this paper, a lightweight model that generates original music content using deep learning along with generating music based on a specific genre is proposed. The paper discusses a lightweight deep learning-based approach for jazz music generation in MIDI format. In this work, the genre of music chosen is Jazz, and the songs selected are classical numbers composed by various artists. All the songs are in MIDI format and there might be differences in the pace or tone of the music. It is prudential to make sure that the chosen datasets that do not have these kinds of differences and are similar to the final output as desired. A model is trained to take in a part of a music file as input and should produce its continuation. The result generated should be similar to the dataset given as the input. Moreover, the proposed model also generates music using a particular instrument.
在当今的现实世界中，对音乐剧难度水平的估计是非常有意义的音乐学习的一部分。没有明确的精确估计，音乐学习者就无法学习。这个问题不是很基础，但由于内容的主观性和数据的稀缺性，它在某种程度上变得很复杂。本文提出了一种使用深度学习生成原创音乐内容并根据特定流派生成音乐的轻量级模型。本文讨论了一种基于轻量级深度学习的 MIDI 格式爵士音乐生成方法。在这项工作中，选择的音乐类型是爵士，选择的歌曲是由不同艺术家创作的经典曲目。所有的歌曲都是 MIDI 格式的，可能在音乐的节奏或音高上存在差异。谨慎起见，选择的数据集不应存在这些差异，并尽可能与最终输出相似。训练一个模型来输入音乐文件的一部分，并生成其延续。生成的结果应该与输入的数据集相似。此外，所提出的模型还可以使用特定的乐器生成音乐。
