43 citations · 68 across the 5 of their papers we have counts for
8 papers
Is Disentanglement enough? On Latent Representations for Controllable Music Generation
Ashis Pati, Alexander Lerch
Improving controllability or the ability to manipulate one or more attributes of the generated data has become a topic of interest in the context of deep generative models of music…
An Interdisciplinary Review of Music Performance Analysis
Alexander Lerch, Claire Arthur, Ashis Pati +1
A musical performance renders an acoustic realization of a musical score or other representation of a composition. Different performances of the same composition may vary in terms…
Score-informed Networks for Music Performance Assessment
Jiawen Huang, Yun-Ning Hung, Ashis Pati +2
The assessment of music performances in most cases takes into account the underlying musical score being performed. While there have been several automatic approaches for objective…
dMelodies: A Music Dataset for Disentanglement Learning
Ashis Pati, Siddharth Gururani, Alexander Lerch
Representation learning focused on disentangling the underlying factors of variation in given data has become an important area of research in machine learning. However, most of th…
Attribute-based Regularization of Latent Spaces for Variational Auto-Encoders
Ashis Pati, Alexander Lerch
Selective manipulation of data attributes using deep generative models is an active area of research. In this paper, we present a novel method to structure the latent space of a Va…
Explicitly Conditioned Melody Generation: A Case Study with Interdependent RNNs
Benjamin Genchel, Ashis Pati, Alexander Lerch
Deep generative models for symbolic music are typically designed to model temporal dependencies in music so as to predict the next musical event given previous events. In many case…