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• W4384080217 endingPage "92549" @default.
• W4384080217 startingPage "92540" @default.
• W4384080217 abstract "Audio denoising is a task to improve the perceptual quality of noisy audio signals. There is still residual noise after the denoising of noisy signals, which will affect the quality of audio data. Traditional and deep learning-based methods are still limited to the manual addition of artificial noise or low-frequency noise. Recently, audio denoising has been transformed into an image segmentation problem, and deep neural networks have been applied to solve this problem. However, its performance is limited to shallow image segmentation models. This paper proposes a novel vision transformer model for visual bird sound denoising, combining a pyramid transformer and DeepLabV3+ network (named PtDeepLab) to filter out the noise. The proposed PtDeepLab model is based on the pyramid transformer, which generates long-range and multi-scale representations. The PtDeepLab model can achieve intuitive noise reduction in audio, which helps to separate clean audio from the mixture signal. Extensive experimental results showed that the proposed model has a better denoising performance than state-of-the-art methods." @default.
• W4384080217 created "2023-07-13" @default.
• W4384080217 creator A5018132970 @default.
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• W4384080217 date "2023-01-01" @default.
• W4384080217 modified "2023-09-23" @default.
• W4384080217 title "DeepLabV3+ Vision Transformer for Visual Bird Sound Denoising" @default.
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• W4384080217 doi "https://doi.org/10.1109/access.2023.3294476" @default.
• W4384080217 hasPublicationYear "2023" @default.
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