A Survey on Artificial Intelligence for Music Generation: Agents, Domains and Perspectives

• URL: http://arxiv.org/abs/2210.13944v1
• Date: Tue, 25 Oct 2022 11:54:30 GMT
• Title: A Survey on Artificial Intelligence for Music Generation: Agents, Domains and Perspectives
• Authors: Carlos Hernandez-Olivan, Javier Hernandez-Olivan, Jose R. Beltran
• Abstract summary: We describe how humans compose music and how new AI systems could imitate such process. To understand how AI models and algorithms generate music, we explore, analyze and describe the agents that take part of the music generation process.
• Score: 10.349825060515181
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Music is one of the Gardner's intelligences in his theory of multiple intelligences. How humans perceive and understand music is still being studied and is crucial to develop artificial intelligence models that imitate such processes. Music generation with Artificial Intelligence is an emerging field that is gaining much attention in the recent years. In this paper, we describe how humans compose music and how new AI systems could imitate such process by comparing past and recent advances in the field with music composition techniques. To understand how AI models and algorithms generate music and the potential applications that might appear in the future, we explore, analyze and describe the agents that take part of the music generation process: the datasets, models, interfaces, the users and the generated music. We mention possible applications that might benefit from this field and we also propose new trends and future research directions that could be explored in the future.

Related papers

• A Survey of Foundation Models for Music Understanding [60.83532699497597]
  This work is one of the early reviews of the intersection of AI techniques and music understanding. We investigated, analyzed, and tested recent large-scale music foundation models in respect of their music comprehension abilities.
  arXiv  Detail & Related papers  (2024-09-15T03:34:14Z)
• Prevailing Research Areas for Music AI in the Era of Foundation Models [8.067636023395236]
  There has been a surge of generative music AI applications within the past few years. We discuss the current state of music datasets and their limitations. We highlight applications of these generative models towards extensions to multiple modalities and integration with artists' workflow.
  arXiv  Detail & Related papers  (2024-09-14T09:06:43Z)
• Foundation Models for Music: A Survey [77.77088584651268]
  Foundations models (FMs) have profoundly impacted diverse sectors, including music. This comprehensive review examines state-of-the-art (SOTA) pre-trained models and foundation models in music.
  arXiv  Detail & Related papers  (2024-08-26T15:13:14Z)
• AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
  generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs. This paper explores the innovative concept of harnessing these AI-generated images as new data sources. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
  arXiv  Detail & Related papers  (2023-10-03T06:55:19Z)
• AI (r)evolution -- where are we heading? Thoughts about the future of music and sound technologies in the era of deep learning [11.809589907446632]
  Report presents a summary of the student debates on the topics of: Data, Impact, and the Environment; Responsible Innovation and Creative Practice; Creativity and Bias; and From Tools to the Singularity. The students represent the future generation of AI and music researchers. The academics represent the incumbent establishment. The student debates reported here capture visions, dreams, concerns, uncertainties, and contentious issues for the future of AI and music as the establishment is rightfully challenged by the next generation.
  arXiv  Detail & Related papers  (2023-09-20T15:53:36Z)
• AI-Based Affective Music Generation Systems: A Review of Methods, and Challenges [0.0]
  Artificial intelligence-based approaches have become popular for creating affective music generation systems. Entertainment, healthcare, and sensor-integrated interactive system design are a few of the areas in which AI-based affective music generation systems may have a significant impact.
  arXiv  Detail & Related papers  (2023-01-10T11:08:39Z)
• Redefining Relationships in Music [55.478320310047785]
  We argue that AI tools will fundamentally reshape our music culture. People working in this space could decrease the possible negative impacts on the practice, consumption and meaning of music.
  arXiv  Detail & Related papers  (2022-12-13T19:44:32Z)
• Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
  We show how AI can empower the IoT to make it faster, smarter, greener, and safer. First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
  arXiv  Detail & Related papers  (2020-11-17T13:14:28Z)
• Artificial Musical Intelligence: A Survey [51.477064918121336]
  Music has become an increasingly prevalent domain of machine learning and artificial intelligence research. This article provides a definition of musical intelligence, introduces a taxonomy of its constituent components, and surveys the wide range of AI methods that can be, and have been, brought to bear in its pursuit.
  arXiv  Detail & Related papers  (2020-06-17T04:46:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.