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Found 5 papers, 1 papers with code
SongGLM: Lyric-to-Melody Generation with 2D Alignment Encoding and Multi-Task Pre-Training
In this paper, we propose SongGLM, a lyric-to-melody generation system that leverages 2D alignment encoding and multi-task pre-training based on the General Language Model (GLM) to guarantee the alignment and harmony between lyrics and melodies.
MuDiT & MuSiT: Alignment with Colloquial Expression in Description-to-Song Generation
This task is aimed at bridging the gap between colloquial language understanding and auditory expression within an AI model, with the ultimate goal of creating songs that accurately satisfy human auditory expectations and structurally align with musical norms.
MelodyGLM: Multi-task Pre-training for Symbolic Melody Generation
In particular, subjective evaluations show that, on the melody continuation task, MelodyGLM gains average improvements of 0. 82, 0. 87, 0. 78, and 0. 94 in consistency, rhythmicity, structure, and overall quality, respectively.
PDAugment: Data Augmentation by Pitch and Duration Adjustments for Automatic Lyrics Transcription
Considering that there is a large amount of ASR training data, a straightforward method is to leverage ASR data to enhance ALT training.
Automatic Lyrics Transcription
Automatic Speech Recognition
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CogALex-V Shared Task: LOPE
Automatic discovery of semantically-related words is one of the most important NLP tasks, and has great impact on the theoretical psycholinguistic modeling of the mental lexicon.
