AI-Based Affective Music Generation Systems: A Review of Methods and Challenges

被引:1
|
作者
Dash, Adyasha [1 ]
Agres, Kathleen [1 ]
机构
[1] Natl Univ Singapore, Singapore, Singapore
关键词
Affect; emotion; music; generative AI; automatic music generation; deep learning; machine learning; EMOTIONS;
D O I
10.1145/3672554
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Music is a powerful medium for altering the emotional state of the listener. In recent years, with significant advancements in computing capabilities, artificial intelligence-based (AI-based) approaches have become popular for creating affective music generation (AMG) systems. Entertainment, healthcare, and sensor-integrated interactive system design are a few of the areas in which AI-based affective music generation (AI-AMG) systems may have a significant impact. Given the surge of interest in this topic, this article aims to provide a comprehensive review of controllable AI-AMG systems. The main building blocks of an AI-AMG system are discussed and existing systems are formally categorized based on the core algorithm used for music generation. In addition, this article discusses the main musical features employed to compose affective music, along with the respective AI-based approaches used for tailoring them. Lastly, the main challenges and open questions in this field, as well as their potential solutions, are presented to guide future research. We hope that this review will be useful for readers seeking to understand the state-of-the-art in AI-AMG systems and gain an overview of the methods used for developing them, thereby helping them explore this field in the future.
引用
收藏
页数:34
相关论文
共 50 条
  • [1] Challenges and Opportunities for Validation of AI-Based New Approach Methods
    Hartung, Thomas
    Kleinstreuer, Nicole
    ALTEX-ALTERNATIVES TO ANIMAL EXPERIMENTATION, 2025, 42 (01) : 3 - 21
  • [2] AI-based Security Design of Mobile Crowdsensing Systems: Review, Challenges and Case Studies
    Zhang, Yuegian
    Kantarci, Burak
    2019 13TH IEEE INTERNATIONAL CONFERENCE ON SERVICE-ORIENTED SYSTEM ENGINEERING (SOSE) / 10TH INTERNATIONAL WORKSHOP ON JOINT CLOUD COMPUTING (JCC) / IEEE INTERNATIONAL WORKSHOP ON CLOUD COMPUTING IN ROBOTIC SYSTEMS (CCRS), 2019, : 17 - 26
  • [3] A Systematic Literature Review on AI-Based Methods and Challenges in Detecting Zero-Day Attacks
    Yee Por, Lip
    Dai, Zhen
    Juan Leem, Siew
    Chen, Yi
    Yang, Jing
    Binbeshr, Farid
    Yuen Phan, Koo
    Soon Ku, Chin
    IEEE ACCESS, 2024, 12 : 144150 - 144163
  • [4] Requirements Engineering Challenges in Building AI-Based Complex Systems
    Belani, Hrvoje
    Vukovic, Marin
    Car, Zeljka
    2019 IEEE 27TH INTERNATIONAL REQUIREMENTS ENGINEERING CONFERENCE WORKSHOPS (REW 2019), 2019, : 252 - 255
  • [5] Challenges and requirements of AI-based waste water treatment systems
    Dalibard, Antoine
    Kriem, Lukas Simon
    Beckett, Marc
    Scherle, Stephan
    Yeh, Yen-Cheng
    Schliessmann, Ursula
    AT-AUTOMATISIERUNGSTECHNIK, 2025, 73 (01) : 40 - 49
  • [6] AI-Based Methods to Resolve Real-Time Scheduling for Embedded Systems: A Review
    Boutekkouk F.
    International Journal of Cognitive Informatics and Natural Intelligence, 2021, 15 (04)
  • [7] Deepfakes in digital media forensics: Generation, AI-based detection and challenges
    Bendiab, Gueltoum
    Haiouni, Houda
    Moulas, Isidoros
    Shiaeles, Stavros
    JOURNAL OF INFORMATION SECURITY AND APPLICATIONS, 2025, 88
  • [8] AI-Based Cybersecurity Systems
    Ogiela, Marek R.
    Ogiela, Lidia
    ADVANCED INFORMATION NETWORKING AND APPLICATIONS, VOL 4, AINA 2024, 2024, 202 : 166 - 173
  • [9] AI-Based Information Systems
    Peter Buxmann
    Thomas Hess
    Jason Bennett Thatcher
    Business & Information Systems Engineering, 2021, 63 : 1 - 4
  • [10] AI-Based Information Systems
    Buxmann, Peter
    Hess, Thomas
    Thatcher, Jason Bennett
    BUSINESS & INFORMATION SYSTEMS ENGINEERING, 2021, 63 (01) : 1 - 4