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pro vyhledávání: '"Elhilali, Mounya"'
Latent diffusion models have shown promising results in text-to-audio (T2A) generation tasks, yet previous models have encountered difficulties in generation quality, computational cost, diffusion sampling, and data preparation. In this paper, we int
Externí odkaz:
http://arxiv.org/abs/2409.10819
In this paper, we introduce SoloAudio, a novel diffusion-based generative model for target sound extraction (TSE). Our approach trains latent diffusion models on audio, replacing the previous U-Net backbone with a skip-connected Transformer that oper
Externí odkaz:
http://arxiv.org/abs/2409.08425
Generative voice technologies are rapidly evolving, offering opportunities for more personalized and inclusive experiences. Traditional one-shot voice conversion (VC) requires a target recording during inference, limiting ease of usage in generating
Externí odkaz:
http://arxiv.org/abs/2406.16314
Auditory Attention Decoding (AAD) algorithms play a crucial role in isolating desired sound sources within challenging acoustic environments directly from brain activity. Although recent research has shown promise in AAD using shallow representations
Externí odkaz:
http://arxiv.org/abs/2311.00814
Common target sound extraction (TSE) approaches primarily relied on discriminative approaches in order to separate the target sound while minimizing interference from the unwanted sources, with varying success in separating the target from the backgr
Externí odkaz:
http://arxiv.org/abs/2310.04567
Sound event detection is an important facet of audio tagging that aims to identify sounds of interest and define both the sound category and time boundaries for each sound event in a continuous recording. With advances in deep neural networks, there
Externí odkaz:
http://arxiv.org/abs/2105.13392
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Publikováno v:
In Biomedical Signal Processing and Control August 2023 85
Autor:
Kothinti, Sandeep, Imoto, Keisuke, Chakrabarty, Debmalya, Sell, Gregory, Watanabe, Shinji, Elhilali, Mounya
Sound event detection is a challenging task, especially for scenes with multiple simultaneous events. While event classification methods tend to be fairly accurate, event localization presents additional challenges, especially when large amounts of l
Externí odkaz:
http://arxiv.org/abs/1811.04048
Akademický článek
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