Guzheng tune progression involves intricately harmonizing melodic motif transitions.
Effectively navigating this vast creative possibility space to expose musically consistent …

Recent deep learning approaches for melody harmonization have achieved remarkable
performance by overcoming the uneven chord distributions of music data. However, most of …

Creating symbolic melodies with machine learning is challenging because it requires an
understanding of musical structure and the handling of inter-dependencies and long-term …

Musics, like languages and genes, evolve through a process of transmission, variation, and
selection. Evolution of musical tune families has been studied qualitatively for over a …

Transformers and variational autoencoders (VAE) have been extensively employed for
symbolic (eg, MIDI) domain music generation. While the former boast an impressive …

Lyrics-to-melody generation is an interesting and challenging topic in AI music research
field. Due to the difficulty of learning the correlations between lyrics and melody, previous …

Although deep learning has revolutionized music generation, existing methods for structured
melody generation follow an end-to-end left-to-right note-by-note generative paradigm and …

Numerous task-specific variants of autoregressive networks have been developed for dance
generation. Nonetheless, a severe limitation remains in that all existing algorithms can …

Music editing primarily entails the modification of instrument tracks or remixing in the whole,
which offers a novel reinterpretation of the original piece through a series of operations …

Regional style in Chinese folk songs is a rich treasure that can be used for ethnic music
creation and folk culture research. In this paper, we propose MG-VAE, a music generative …