Automatic score-to-score music generation

Music generation is the task of generating music using a model or algorithm. There are multiple ways of achieving this task as there are multiple types of data to rep-resent music. Music generation can be audio-based or with symbolic music such as MIDI data. Approaches with symbolic music have been successful, especially using note-level representation such as the MIDI format. However, there is an absence of a baseline dataset tailored specifically for music scores generation using notation-level representations. In this thesis, we first construct a dataset specifically for the training and the evaluation of music generation models, then we build an automatic score-to-score generation model to generate scores. This research not only expands the horizons of music score generation but also establishes a solid foundation for future innovations in the field with a dataset made for score-to-score music genera-tion