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- W4328005956 abstract "Melody generation is a fundamental operation in Algorithmic Music Composition. The previous models are usually based on absolute pitch, and the data representation is sparse, which is not conducive to feature extraction. To solve the problem and generate a novel and tuneful melody, we design an interval-based representation, including interval sequence and Position-Memory Embedding. However, the melody tends to get offset when generating the interval sequence. To address the offset issue, we introduce Reinforcement Learning (RL) and propose Observer-Generator-Critic (OGC) framework based on RL for melody generation. Compared with the recently proposed models, our model achieves competitive results in user study. We also design a series of objective experimental verification for the above result." @default.
- W4328005956 created "2023-03-22" @default.
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- W4328005956 date "2022-08-01" @default.
- W4328005956 modified "2023-09-27" @default.
- W4328005956 title "Interval-based melody generation via reinforcement learning" @default.
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- W4328005956 doi "https://doi.org/10.1109/bigcom57025.2022.00031" @default.
- W4328005956 hasPublicationYear "2022" @default.
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