Computational models of expressive piano performance rely on attributes like tempo, timing,
dynamics and pedalling. Despite some promising models for performance assessment and …

Recent neural-based synthesis models have achieved impressive results for musical
instrument sound generation. In particular, the Differentiable Digital Signal Processing …

Driven by recent advancements in Extended Reality (XR), the hype around the Metaverse,
and real‐time computer graphics, the transformation of the performing arts, particularly in …

Data is the lifeblood of modern machine learning systems, including for those in Music
Information Retrieval (MIR). However, MIR has long been mired by small datasets and …

Synthesizing performing guitar sound is a highly challenging task due to the polyphony and
high variability in expression. Recently, deep generative models have shown promising …

Computational aesthetic evaluation has made remarkable contribution to visual art works,
but its application to music is still rare. Currently, subjective evaluation is still the most …

Instrument sound synthesis using deep neural networks has received numerous
improvements over the last couple of years. Among them, the Differentiable Digital Signal …

Synthesizing high-quality singing voice from music score is a challenging problem in music
generation and has many practical applications. Samples generated by existing singing …

With the similarity between music and speech synthesis from symbolic input and the rapid
development of text-to-speech (TTS) techniques, it is worthwhile to explore ways to improve …

Recent advances in data-driven expressive performance rendering have enabled automatic
models to reproduce the characteristics and the variability of human performances of …