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Found 7 papers, 4 papers with code
Creative divergent synthesis with generative models
Machine learning approaches now achieve impressive generation capabilities in numerous domains such as image, audio or video.
Challenges in creative generative models for music: a divergence maximization perspective
The development of generative Machine Learning (ML) models in creative practices, enabled by the recent improvements in usability and availability of pre-trained models, is raising more and more interest among artists, practitioners and performers.
Diet deep generative audio models with structured lottery
This hypothesis states that extremely efficient small sub-networks exist in deep models and would provide higher accuracy than larger models if trained in isolation.
Cross-modal variational inference for bijective signal-symbol translation
Extraction of symbolic information from signals is an active field of research enabling numerous applications especially in the Musical Information Retrieval domain.
Universal audio synthesizer control with normalizing flows
By using this formulation, we show that we can address simultaneously automatic parameter inference, macro-control learning and audio-based preset exploration within a single model.
Modulated Variational auto-Encoders for many-to-many musical timbre transfer
We define timbre transfer as applying parts of the auditory properties of a musical instrument onto another.
Sound Audio and Speech Processing
Generative timbre spaces: regularizing variational auto-encoders with perceptual metrics
Based on this, we introduce a method for descriptor-based synthesis and show that we can control the descriptors of an instrument while keeping its timbre structure.
Sound Audio and Speech Processing
