| dc.description.abstract | Automatic music generation refers to the process of creating music using computer algorithms and artificial intelligence techniques. It has a long history and can be traced back to the mid-20th century. Researchers have employed various methods and techniques over the years, ranging from rule-based systems and evolutionary algorithms to the emergence of machine learning and neural networks. Automatic music generation has made significant progress, enabling the generation of more creative and diverse music compositions. Furthermore, the impact of digital audio processing technology has made music analysis and transformation easier and more accurate.
Automatic music generation has a wide range of applications. It not only provides new possibilities and sources of inspiration for music composition but also helps save time and resources by quickly generating music compositions that meet specific requirements.
In the context of neural networks, Gatys et al. [1] introduced the term ＂style transfer,＂ which typically refers to preserving the explicit content features of an image and applying the salient style features of another image to it. In the case of music, style transfer involves separating and recombining the musical content and musical style of different music compositions to generate novel music with creative and synthesized characteristics. Music style can refer to different levels of musical features, and the boundary between content and style is highly dynamic, depending on factors such as timbre, performance style, or compositional objectives, which are associated with different style transfer problems [2].
This study proposes a method for music style transfer using the Generative Adversarial Network (GAN) framework [3]. We transform music preprocessing into pianoroll images, treating music as images, and utilize the CycleGAN model [4] for style transfer of musical phrases and complete compositions. This allows users to provide only musical phrases and obtain corresponding complete compositions. In the implementation of the method, we not only employ deep learning frameworks but also utilize domain-specific music knowledge for data processing to further analyze and optimize the transformation results, enhancing the quality and practicality of the conversion. We also compare the performance of different generator and discriminator architectures on our dataset.
The advantage of this method lies in its ability to automatically generate corresponding complete compositions, thus enhancing the practicality of music style transfer. This research provides new ideas and methods for the field of music style transfer and automatic music generation. Future research directions can further explore transformations between different music styles and apply them to music composition, music education, and other domains, enriching and expanding the possibilities of music creation. | en_US |