Publication Type
Master Thesis
Version
publishedVersion
Publication Date
11-2021
Abstract
In this thesis, I focus on the music generation conditional on human sentiments such as positive and negative. As there are no existing large-scale music datasets annotated with sentiment labels, generating high-quality music conditioned on sentiments is hard. I thus build a new dataset consisting of the triplets of lyric, melody and sentiment, without requiring any manual annotations. I utilize an automated sentiment recognition model (based on the BERT trained on Edmonds Dance dataset) to "label'' the music according to the sentiments recognized from its lyrics. I then train the model of generating sentimental music and call the method Sentimental Lyric and Melody Generator (SLMG). Specifically, SLMG is consisted of three modules: 1) an encoder-decoder model trained end-to-end for generating lyric and melody; 2) a music sentiment classifier trained on labelled data; and 3) a modified beam search algorithm that guides the music generation process by incorporating the music sentiment classifier. I conduct subjective and objective evaluations on the generated music and the evaluation results show that SLMG is capable of generating tuneful lyric and melody with specific sentiments.
Keywords
Conditional Music Generation, Seq2Seq, Beam Search, Transformer
Degree Awarded
MSc in Applied Finance (SUFE)
Discipline
Artificial Intelligence and Robotics | Music
Supervisor(s)
SUN, Qianru
First Page
1
Last Page
78
Publisher
Singapore Management University
City or Country
Singapore
Citation
BAO, Chunhui.
Generating music with sentiments. (2021). 1-78.
Available at: https://ink.library.smu.edu.sg/etd_coll/374
Copyright Owner and License
Author
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.