Music plays an important role in our daily life. With the development of deep learning and modern generation techniques, researchers have done plenty of works on automatic music generation. However, due to the special requirements of both melody and arrangement, most of these methods have limitations when applying to multi-track music generation. Some critical factors related to the quality of music are not well addressed, such as chord progression, rhythm pattern, and musical style. In order to tackle the problems and ensure the harmony of multi-track music, in this article, we propose an end-to-end melody and arrangement generation framework to generate a melody track with several accompany tracks played by some different instruments. To be specific, we first develop a novel Chord based Rhythm and Melody Cross-Generation Model to generate melody with a chord progression. Then, we propose a Multi-Instrument Co-Arrangement Model based on multi-task learning for multi-track music arrangement. Furthermore, to control the musical style of arrangement, we design a Multi-Style Multi-Instrument Co-Arrangement Model to learn the musical style with adversarial training. Therefore, we can not only maintain the harmony of the generated music but also control the musical style for better utilization. Extensive experiments on a real-world dataset demonstrate the superiority and effectiveness of our proposed models.

With the development of knowledge of music composition and the recent increase in demand, an increasing number of companies and research institutes have begun to study the automatic generation of music. However, previous models have limitations when applying to song generation, which requires both the melody and arrangement. Besides, many critical factors related to the quality of a song such as chord progression and rhythm patterns are not well addressed. In particular, the problem of how to ensure the harmony of multi-track music is still underexplored. To this end, we present a focused study on pop music generation, in which we take both chord and rhythm influence of melody generation and the harmony of music arrangement into consideration. We propose an end-to-end melody and arrangement generation framework, called XiaoIce Band, which generates a melody track with several accompany tracks played by several types of instruments. Specifically, we devise a Chord based Rhythm and Melody Cross-Generation Model (CRMCG) to generate melody with chord progressions. Then, we propose a Multi-Instrument Co-Arrangement Model (MICA) using multi-task learning for multi-track music arrangement. Finally, we conduct extensive experiments on a real-world dataset, where the results demonstrate the effectiveness of XiaoIce Band.