研究目的
To develop a real-time emotion-based music accompaniment system that reduces the time and cost associated with creating background music by engaging sound recordists and instrumental performers.
研究成果
The proposed music accompaniment system, utilizing fuzzy logic and APEGA, effectively generates music in real time that aligns with specified emotions. The system's ability to adjust tempo smoothly and generate melodies quickly makes it practical for various applications, including video soundtracks and live performances. Future work could focus on enhancing the system's adaptability to a wider range of emotional expressions and further optimizing the algorithm for faster convergence.
研究不足
The study does not address the subjective nature of musical preference and the challenge of measuring the artistic value of generated music. Additionally, the system's performance in generating music for all possible emotions may vary, and further optimization is needed for certain emotional expressions.
1：Experimental Design and Method Selection:
The study employs a fuzzy logic controller to adjust music tempo and an adaptive partition evolutionary genetic algorithm (APEGA) for melody generation. Chord progressions are generated using music theory, and instrumentation is determined probabilistically.
2：Sample Selection and Data Sources:
The system uses Virtual Studio Technology (VST) for real-time music output, allowing users to listen to composing results directly.
3：List of Experimental Equipment and Materials:
The system interface includes MIDI port selection, tempo adjusting bar, Start/Stop buttons, and a mixer for volume balance. Instruments include pad, melody, guitar, bass, and drum units with various playing styles and patterns.
4：Experimental Procedures and Operational Workflow:
The system processes emotion inputs to adjust music tempo and instrument volumes, generates chord progressions and melodies, and outputs music in real time through VST.
5：Data Analysis Methods:
The performance of APEGA is compared with other intelligent algorithms like GA, PSO, EPSO, and GA-PSO through learning curves and resulting melodies analysis.
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