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pro vyhledávání: '"Simon, Ian"'
Autor:
Donahue, Chris, Caillon, Antoine, Roberts, Adam, Manilow, Ethan, Esling, Philippe, Agostinelli, Andrea, Verzetti, Mauro, Simon, Ian, Pietquin, Olivier, Zeghidour, Neil, Engel, Jesse
We present SingSong, a system that generates instrumental music to accompany input vocals, potentially offering musicians and non-musicians alike an intuitive new way to create music featuring their own voice. To accomplish this, we build on recent d
Externí odkaz:
http://arxiv.org/abs/2301.12662
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 unreliable labels. In this work, we propose to break this bottleneck using gene
Externí odkaz:
http://arxiv.org/abs/2209.14458
Autor:
Hawthorne, Curtis, Simon, Ian, Roberts, Adam, Zeghidour, Neil, Gardner, Josh, Manilow, Ethan, Engel, Jesse
An ideal music synthesizer should be both interactive and expressive, generating high-fidelity audio in realtime for arbitrary combinations of instruments and notes. Recent neural synthesizers have exhibited a tradeoff between domain-specific models
Externí odkaz:
http://arxiv.org/abs/2206.05408
Autor:
Hawthorne, Curtis, Jaegle, Andrew, Cangea, Cătălina, Borgeaud, Sebastian, Nash, Charlie, Malinowski, Mateusz, Dieleman, Sander, Vinyals, Oriol, Botvinick, Matthew, Simon, Ian, Sheahan, Hannah, Zeghidour, Neil, Alayrac, Jean-Baptiste, Carreira, João, Engel, Jesse
Real-world data is high-dimensional: a book, image, or musical performance can easily contain hundreds of thousands of elements even after compression. However, the most commonly used autoregressive models, Transformers, are prohibitively expensive t
Externí odkaz:
http://arxiv.org/abs/2202.07765
Automatic Music Transcription (AMT), inferring musical notes from raw audio, is a challenging task at the core of music understanding. Unlike Automatic Speech Recognition (ASR), which typically focuses on the words of a single speaker, AMT often requ
Externí odkaz:
http://arxiv.org/abs/2111.03017
Automatic Music Transcription has seen significant progress in recent years by training custom deep neural networks on large datasets. However, these models have required extensive domain-specific design of network architectures, input/output represe
Externí odkaz:
http://arxiv.org/abs/2107.09142
Score-based generative models and diffusion probabilistic models have been successful at generating high-quality samples in continuous domains such as images and audio. However, due to their Langevin-inspired sampling mechanisms, their application to
Externí odkaz:
http://arxiv.org/abs/2103.16091
We consider the problem of learning high-level controls over the global structure of generated sequences, particularly in the context of symbolic music generation with complex language models. In this work, we present the Transformer autoencoder, whi
Externí odkaz:
http://arxiv.org/abs/1912.05537
Publikováno v:
In Science of the Total Environment 15 September 2023 891
Autor:
Hawthorne, Curtis, Stasyuk, Andriy, Roberts, Adam, Simon, Ian, Huang, Cheng-Zhi Anna, Dieleman, Sander, Elsen, Erich, Engel, Jesse, Eck, Douglas
Generating musical audio directly with neural networks is notoriously difficult because it requires coherently modeling structure at many different timescales. Fortunately, most music is also highly structured and can be represented as discrete note
Externí odkaz:
http://arxiv.org/abs/1810.12247