Zobrazeno 1 - 10
of 204
pro vyhledávání: '"Bryan, Nicholas"'
Autor:
Novack, Zachary, Zhu, Ge, Casebeer, Jonah, McAuley, Julian, Berg-Kirkpatrick, Taylor, Bryan, Nicholas J.
Despite advances in diffusion-based text-to-music (TTM) methods, efficient, high-quality generation remains a challenge. We introduce Presto!, an approach to inference acceleration for score-based diffusion transformers via reducing both sampling ste
Externí odkaz:
http://arxiv.org/abs/2410.05167
Controllable music generation methods are critical for human-centered AI-based music creation, but are currently limited by speed, quality, and control design trade-offs. Diffusion Inference-Time T-optimization (DITTO), in particular, offers state-of
Externí odkaz:
http://arxiv.org/abs/2405.20289
Diffusion-based audio and music generation models commonly perform generation by constructing an image representation of audio (e.g., a mel-spectrogram) and then convert it to audio using a phase reconstruction model or vocoder. Typical vocoders, how
Externí odkaz:
http://arxiv.org/abs/2403.10493
We introduce a new online adaptive filtering method called supervised multi-step adaptive filters (SMS-AF). Our method uses neural networks to control or optimize linear multi-delay or multi-channel frequency-domain filters and can flexibly scale-up
Externí odkaz:
http://arxiv.org/abs/2403.00977
We propose Diffusion Inference-Time T-Optimization (DITTO), a general-purpose frame-work for controlling pre-trained text-to-music diffusion models at inference-time via optimizing initial noise latents. Our method can be used to optimize through any
Externí odkaz:
http://arxiv.org/abs/2401.12179
Text-to-music generation models are now capable of generating high-quality music audio in broad styles. However, text control is primarily suitable for the manipulation of global musical attributes like genre, mood, and tempo, and is less suitable fo
Externí odkaz:
http://arxiv.org/abs/2311.07069
Adaptive filters are applicable to many signal processing tasks including acoustic echo cancellation, beamforming, and more. Adaptive filters are typically controlled using algorithms such as least-mean squares(LMS), recursive least squares(RLS), or
Externí odkaz:
http://arxiv.org/abs/2209.09955
We present a framework that can impose the audio effects and production style from one recording to another by example with the goal of simplifying the audio production process. We train a deep neural network to analyze an input recording and a style
Externí odkaz:
http://arxiv.org/abs/2207.08759
Adaptive filtering algorithms are pervasive throughout signal processing and have had a material impact on a wide variety of domains including audio processing, telecommunications, biomedical sensing, astrophysics and cosmology, seismology, and many
Externí odkaz:
http://arxiv.org/abs/2204.11942
Autor:
Anthony Ssebagereka, Gatien de Broucker, Elizabeth Ekirapa-Kiracho, Rornald Muhumuza Kananura, Alfred Driwale, Joshua Mak, Aloysius Mutebi, Bryan Nicholas Patenaude
Publikováno v:
BMC Public Health, Vol 24, Iss 1, Pp 1-13 (2024)
Abstract Background This study analyses vaccine coverage and equity among children under five years of age in Uganda based on the 2016 Uganda Demographic and Health Survey (UDHS) dataset. Understanding equity in vaccine access and the determinants is
Externí odkaz:
https://doaj.org/article/aa7254d6ec5141408d59512aadd2e0bd
