RL-Chord: CLSTM-Based Melody Harmonization Using Deep Reinforcement Learning

Ji, Shulei; Yang, Xinyu; Luo, Jing; Li, Juan

doi:10.1109/tnnls.2023.3248793

Cited by 3 publications

(1 citation statement)

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“…This model can generate music in response to input, like a human musician improvising in real time. RL-Chord [19] is a melody harmonization system using RL and conditional LSTM (CLSTM) to generate chord progression. Ref.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Crafting Creative Melodies: A User-Centric Approach for Symbolic Music Generation

Dadman,

Bremdal

2024

Electronics

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Composing coherent and structured music is one of the main challenges in symbolic music generation. Our research aims to propose a user-centric framework design that promotes a collaborative environment between users and knowledge agents. The primary objective is to improve the music creation process by actively involving users who provide qualitative feedback and emotional assessments. The proposed framework design constructs an abstract format in which a musical piece is represented as a sequence of musical samples. It consists of multiple agents that embody the dynamics of musical creation, emphasizing user-driven creativity and control. This user-centric approach can benefit individuals with different musical backgrounds, encouraging creative exploration and autonomy in personalized, adaptive environments. To guide the design of this framework, we investigate several key research questions, including the optimal balance between system autonomy and user involvement, the extraction of rhythmic and melodic features through musical sampling, and the effectiveness of topological and hierarchical data representations. Our discussion will highlight the different aspects of the framework in relation to the research questions, expected outcomes, and its potential effectiveness in achieving objectives. Through establishing a theoretical foundation and addressing the research questions, this work has laid the groundwork for future empirical studies to validate the framework and its potential in symbolic music generation.

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Section: Reinforcement Learningmentioning

confidence: 99%