Kavli Affiliate: Jing Wang
| First 5 Authors: Jiatao Chen, Tianming Xie, Xing Tang, Jing Wang, Wenjing Dong
| Summary:
In recent years, deep learning has significantly advanced the MIDI domain,
solidifying music generation as a key application of artificial intelligence.
However, existing research primarily focuses on Western music and encounters
challenges in generating melodies for Chinese traditional music, especially in
capturing modal characteristics and emotional expression. To address these
issues, we propose a new architecture, the Dual-Feature Modeling Module, which
integrates the long-range dependency modeling of the Mamba Block with the
global structure capturing capabilities of the Transformer Block. Additionally,
we introduce the Bidirectional Mamba Fusion Layer, which integrates local
details and global structures through bidirectional scanning, enhancing the
modeling of complex sequences. Building on this architecture, we propose the
REMI-M representation, which more accurately captures and generates modal
information in melodies. To support this research, we developed FolkDB, a
high-quality Chinese traditional music dataset encompassing various styles and
totaling over 11 hours of music. Experimental results demonstrate that the
proposed architecture excels in generating melodies with Chinese traditional
music characteristics, offering a new and effective solution for music
generation.
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