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Found 8 papers, 5 papers with code
Is Disentanglement enough? On Latent Representations for Controllable Music Generation
The structure of the latent space with respect to the VAE-decoder plays an important role in boosting the ability of a generative model to manipulate different attributes.
Score-informed Networks for Music Performance Assessment
The assessment of music performances in most cases takes into account the underlying musical score being performed.
dMelodies: A Music Dataset for Disentanglement Learning
In this paper, we present a new symbolic music dataset that will help researchers working on disentanglement problems demonstrate the efficacy of their algorithms on diverse domains.
Attribute-based Regularization of Latent Spaces for Variational Auto-Encoders
Selective manipulation of data attributes using deep generative models is an active area of research.
Explicitly Conditioned Melody Generation: A Case Study with Interdependent RNNs
In this study, we investigate the effects of explicitly conditioning deep generative models with musically relevant information.
Learning to Traverse Latent Spaces for Musical Score Inpainting
The designed model takes both past and future musical context into account and is capable of suggesting ways to connect them in a musically meaningful manner.
Music Performance Analysis: A Survey
Music Information Retrieval (MIR) tends to focus on the analysis of audio signals.
A Rule-Based Computational Model of Cognitive Arithmetic
This area of research explores the retrieval mechanisms and strategies used by people during a common cognitive task.
