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- AI-Lyricist44
AI-Lyricist is proposed, a system to generate novel yet meaningful lyrics given a required vocabulary and a MIDI file as inputs, and its superior performance against the state-of-the-art for the proposed tasks is shown.
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- Content based User Preference Modeling in Music Generation6
An explainable music distance measure is proposed as a bridge between the UMP and AMG and is employed to adjust the AMG's parameters which control the music generation process in an iterative manner, so that the generated song will be closer to the user's UMP in every iteration.
- XAI-Lyricist: Improving the Singability of AI-Generated Lyrics with Prosody Explanations5
XAI-Lyricist, leveraging musical prosody to guide LMs in generating singable lyrics and providing human-understandable singability explanations, shows that musical prosody can significantly improve the singability of LM-generated lyrics.
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- Joint Learning of Wording and Formatting for Singable Melody-to-Lyric Generation4
This work aims to narrow the gap between machine-generated lyrics and those written by human lyricists by jointly learning both wording and formatting for melody-to-lyric generation by introducing multiple auxiliary supervision objective informed by musicological findings on melody--lyric relationships.
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- KeYric: Unsupervised Keywords Extraction and Expansion from Music for Coherent Lyrics Generation2
KeYric is a novel system that leverages keyword skeletons to strengthen both coherence and musicality in lyrics generation, and indicates that integrating genre-relevant elements, such as pitch, into music encoding is crucial, as musical genres significantly affect lyric coherence.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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