What is it about?

This paper attempts to provide a task-oriented survey of symbolic music generation based on deep learning techniques, covering most of the currently popular music generation tasks. The distinct models under the same task are set forth briefly and strung according to their motivations, basically in chronological order. Moreover, we summarize the common datasets suitable for various tasks, discuss the music representations and the evaluation methods, highlight current challenges in symbolic music generation, and finally point out potential future research directions.

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Why is it important?

This survey summarizes various methods under the same music generation subtask so that researchers can quickly grasp the current situation and learn from other methods, which brings great convenience for follow-up studies. According to whether the music generation has conditions, controls, performance characteristics, or interaction with people, symbolic music generation is divided into five categories: generation from scratch, conditional generation, controllable generation, performance generation, and interactive generation. Each generation task is further divided into finer-grained subtasks.

Perspectives

The focus of this survey is on different generation tasks rather than concrete algorithms or models. Thus, people can have a comprehensive and profound understanding of what tasks have appeared in this field, where they have developed, and what shortcomings and challenges these tasks exist, thereby laying the foundation for breakthroughs in this field. This task-oriented taxonomy brings convenience to researchers, as they can quickly gain an overall understanding of the task that they are interested in. The proposed challenges and potential future directions may bring more inspiration to researchers.

Shulei Ji
Xi'an Jiaotong University

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This page is a summary of: A Survey on Deep Learning for Symbolic Music Generation: Representations, Algorithms, Evaluations, and Challenges, ACM Computing Surveys, May 2023, ACM (Association for Computing Machinery),
DOI: 10.1145/3597493.
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