首页|Study Results from University of New South Wales in the Area of Machine Learning Published (Comparative analysis of voice denoising using machine learning and traditional denoising)
Study Results from University of New South Wales in the Area of Machine Learning Published (Comparative analysis of voice denoising using machine learning and traditional denoising)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Fresh data on artificial intelligence are presented in a new report. According to news reporting originating from the University of New South Wales by NewsRx correspondents, research stated, “Noise often affects the content of an audio signal, and noise reduction techniques can help retrieve the original speech content.” Our news editors obtained a quote from the research from University of New South Wales: “In recent years, AI-based noise reduction has witnessed rapid development. This article provides a brief introduction to the background and principles of several AI-based noise reduction methods. One of the mentioned methods is an end-to-end time-domain deep learning speech division algorithm, which utilizes a multi-layer CNN network framework. Due to the need for deep network architectures to extract features, it involves a higher computational load. Traditional noise reduction algorithms, on the other hand, are based on researchers’ understanding of noise patterns and modeling. Traditional methods may not perform well on non-stationary noise, but they are relatively simple in terms of algorithmic implementation.”
University of New South WalesCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
