Deep Attention-Based Alignment Network for Melody Generation from Incomplete Lyrics
This addresses the challenge of automated music composition for creators, but it appears incremental as it builds on existing encoder-decoder and attention methods for a specific domain.
The paper tackles the problem of generating complete lyrics and corresponding melodies from incomplete seed lyrics, using a deep attention-based alignment network to achieve this in an encoder-decoder framework, with qualitative and quantitative evaluations showing it can generate proper lyrics and melodies.
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.