AffectMachine-Pop: A controllable expert system for real-time pop music generation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908413108158464 |
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| author | Agres, Kat R. Dash, Adyasha Chua, Phoebe Ehrlich, Stefan K. |
| author_facet | Agres, Kat R. Dash, Adyasha Chua, Phoebe Ehrlich, Stefan K. |
| contents | Music is a powerful medium for influencing listeners' emotional states, and this capacity has driven a surge of research interest in AI-based affective music generation in recent years. Many existing systems, however, are a black box which are not directly controllable, thus making these systems less flexible and adaptive to users. We present \textit{AffectMachine-Pop}, an expert system capable of generating retro-pop music according to arousal and valence values, which can either be pre-determined or based on a listener's real-time emotion states. To validate the efficacy of the system, we conducted a listening study demonstrating that AffectMachine-Pop is capable of generating affective music at target levels of arousal and valence. The system is tailored for use either as a tool for generating interactive affective music based on user input, or for incorporation into biofeedback or neurofeedback systems to assist users with emotion self-regulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08200 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AffectMachine-Pop: A controllable expert system for real-time pop music generation Agres, Kat R. Dash, Adyasha Chua, Phoebe Ehrlich, Stefan K. Human-Computer Interaction Multimedia Music is a powerful medium for influencing listeners' emotional states, and this capacity has driven a surge of research interest in AI-based affective music generation in recent years. Many existing systems, however, are a black box which are not directly controllable, thus making these systems less flexible and adaptive to users. We present \textit{AffectMachine-Pop}, an expert system capable of generating retro-pop music according to arousal and valence values, which can either be pre-determined or based on a listener's real-time emotion states. To validate the efficacy of the system, we conducted a listening study demonstrating that AffectMachine-Pop is capable of generating affective music at target levels of arousal and valence. The system is tailored for use either as a tool for generating interactive affective music based on user input, or for incorporation into biofeedback or neurofeedback systems to assist users with emotion self-regulation. |
| title | AffectMachine-Pop: A controllable expert system for real-time pop music generation |
| topic | Human-Computer Interaction Multimedia |
| url | https://arxiv.org/abs/2506.08200 |