Zobrazeno 1 - 10
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pro vyhledávání: '"Pati, Ashis"'
Autor:
Pati, Ashis, Lerch, Alexander
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. Recent attempts in this direction have relied on learning disentangl
Externí odkaz:
http://arxiv.org/abs/2108.01450
Publikováno v:
Transactions of the International Society for Music Information Retrieval, 3(1), pp.221-245, 2020
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 of performance parameters such as timing or dynamics, and these variat
Externí odkaz:
http://arxiv.org/abs/2104.09018
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 music performance assessment (MPA) based on extracted features from b
Externí odkaz:
http://arxiv.org/abs/2008.00203
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 the studies in this area have relied on datasets from the computer visio
Externí odkaz:
http://arxiv.org/abs/2007.15067
Autor:
Pati, Ashis, Lerch, Alexander
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 Variational Auto-Encoder (VAE) to encode different continuous-valued att
Externí odkaz:
http://arxiv.org/abs/2004.05485
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 cases, such models are expected to learn abstract concepts such as harmony
Externí odkaz:
http://arxiv.org/abs/1907.05208
Publikováno v:
20th International Society for Music Information Retrieval Conference (ISMIR), 2019, Delft, The Netherlands
Music Inpainting is the task of filling in missing or lost information in a piece of music. We investigate this task from an interactive music creation perspective. To this end, a novel deep learning-based approach for musical score inpainting is pro
Externí odkaz:
http://arxiv.org/abs/1907.01164
Music Information Retrieval (MIR) tends to focus on the analysis of audio signals. Often, a single music recording is used as representative of a "song" even though different performances of the same song may reveal different properties. A performanc
Externí odkaz:
http://arxiv.org/abs/1907.00178
Cognitive arithmetic studies the mental processes used in solving math problems. This area of research explores the retrieval mechanisms and strategies used by people during a common cognitive task. Past research has shown that human performance in a
Externí odkaz:
http://arxiv.org/abs/1705.01208
Autor:
Pati, Ashis1 (AUTHOR) ashis.pati@gatech.edu, Lerch, Alexander1 (AUTHOR)
Publikováno v:
Neural Computing & Applications. May2021, Vol. 33 Issue 9, p4429-4444. 16p.
