This paper introduces four different artificial intelligence algorithms for music generation and aims to compare these methods not only based on the aesthetic quality of the generated music but also on their suitability for specific applications. The first set of melodies is produced by a slightly modified visual transformer neural network that is used as a language model. The second set of melodies is generated by combining chat sonification with a classic transformer neural network (the same method of music generation is presented in a previous research), the third set of melodies is generated by combining the Schillinger rhythm theory together with a classic transformer neural network, and the fourth set of melodies is generated using GPT3 transformer provided by OpenAI. A comparative analysis is performed on the melodies generated by these approaches and the results indicate that significant differences can be observed between them and regarding the aesthetic value of them, GPT3 produced the most pleasing melodies, and the newly introduced Schillinger method proved to generate better sounding music than previous sonification methods.

翻译：本文介绍了四种不同的人工智能音乐生成算法，旨在从生成音乐的美学质量及其对特定应用的适用性两方面对这些方法进行比较。第一组旋律由经过轻微修改的视觉Transformer神经网络（用作语言模型）生成。第二组旋律通过将聊天可听化与经典Transformer神经网络相结合生成（该方法在先前研究中已有介绍）；第三组旋律通过将席林格节奏理论与经典Transformer神经网络相结合生成；第四组旋律使用OpenAI提供的GPT3 Transformer生成。对这些方法生成的旋律进行的比较分析表明，各方法之间存在显著差异。在美学价值方面，GPT3生成的旋律最令人愉悦，而新引入的席林格方法被证明能生成比先前可听化方法音效更佳的音乐。