LyricJam: A system for generating lyrics for live instrumental music
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Abstract (arXiv)
We describe a real-time system that receives a live audio stream from a jam session and generates lyric lines that are congruent with the live music being played. Two novel approaches are proposed to align the learned latent spaces of audio and text representations that allow the system to generate novel lyric lines matching live instrumental music. One approach is based on adversarial alignment of latent representations of audio and lyrics, while the other approach learns to transfer the topology from the music latent space to the lyric latent space. A user study with music artists using the system showed that the system was useful not only in lyric composition, but also encouraged the artists to improvise and find new musical expressions. Another user study demonstrated that users preferred the lines generated using the proposed methods to the lines generated by a baseline model.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 15 of its 40 references that are in the map. First posted on arXiv 3 Jun 2021.
Builds on
15 of 40 references.
References · 15
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 1.
3 papers.
- 2026CoLyricist: Enhancing Lyric Writing with AI through Workflow-Aligned Support26 Feb 2026
- 2025Between Predictability and Randomness: Seeking Artistic Inspiration from AI Generative Models14 Jun 2025
- 2022LyricJam Sonic: A Generative System for Real-Time Composition and Musical Improvisation27 Oct 2022