Deep Attention-Based Alignment Network for Melody Generation from Incomplete Lyrics
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Abstract (arXiv)
We propose a deep attention-based alignment network, which aims to automatically predict lyrics and melody with given incomplete lyrics as input in a way similar to the music creation of humans. Most importantly, a deep neural lyrics-to-melody net is trained in an encoder-decoder way to predict possible pairs of lyrics-melody when given incomplete lyrics (few keywords). The attention mechanism is exploited to align the predicted lyrics with the melody during the lyrics-to-melody generation. The qualitative and quantitative evaluation metrics reveal that the proposed method is indeed capable of generating proper lyrics and corresponding melody for composing new songs given a piece of incomplete seed lyrics.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 19 references that are in the map. First posted on arXiv 23 Jan 2023.
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References · 12
- 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
- 2017MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompanimentcited by 151
- 2016C-RNN-GAN: Continuous recurrent neural networks with adversarial trainingcited by 132
- 2020SongMASS: Automatic Song Writing with Pre-training and Alignment Constraintcited by 41
- 2019Conditional LSTM-GAN for Melody Generation from Lyricscited by 31
- 2016Song From PI: A Musically Plausible Network for Pop Music Generationcited by 13
- 2018Neural Melody Composition from Lyricscited by 10
- 2017Deep Cross-Modal Correlation Learning for Audio and Lyrics in Music Retrievalcited by 6
- 2016Algorithmic Songwriting with ALYSIAcited by 5
- 2022Interpretable Melody Generation from Lyrics with Discrete-Valued Adversarial Trainingcited by 2
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