论文标题
GGA-MG：音乐发电的生成遗传算法
GGA-MG: Generative Genetic Algorithm for Music Generation
论文作者
论文摘要
音乐发电（MG）是一个有趣的研究主题，它将音乐和人工智能的艺术（AI）联系起来。目的是训练人造作曲家生成无限，新鲜和愉悦的音乐作品。音乐具有不同的部分，例如旋律，和谐和节奏。在本文中，我们提出了一种生成遗传算法（GGA）自动产生旋律。主要的GGA使用长期记忆（LSTM）复发性神经网络作为目标函数，应通过一系列不良良好的旋律训练。这些旋律必须由另一个具有不同目标函数的GGA提供。 Campins Collection提供了良好的旋律。我们也考虑了这项工作的节奏。实验结果清楚地表明，所提出的GGA方法能够通过自然过渡生成合格的旋律，并且没有节奏误差。
Music Generation (MG) is an interesting research topic that links the art of music and Artificial Intelligence (AI). The goal is to train an artificial composer to generate infinite, fresh, and pleasurable musical pieces. Music has different parts such as melody, harmony, and rhythm. In this paper, we propose a Generative Genetic Algorithm (GGA) to produce a melody automatically. The main GGA uses a Long Short-Term Memory (LSTM) recurrent neural network as the objective function, which should be trained by a spectrum of bad-to-good melodies. These melodies have to be provided by another GGA with a different objective function. Good melodies have been provided by CAMPINs collection. We have considered the rhythm in this work, too. The experimental results clearly show that the proposed GGA method is able to generate eligible melodies with natural transitions and without rhythm error.
