Abstract
When conducting voice interactions, it is found that loud noises have terrible effects on the recognition of industrial robots. Some industrial machines like motors and fans, tend to produce loud noises when working, placing great obstacles to the voice interactions. Therefore, an end-to-end full convolutional network model, called Res-Unet, is proposed to solve this problem. The difference between this network and other conventional ones is the accession of residual networks to the encoder and the decoder. It has increased the convergence and complexity of the network and improved the expression ability of the network. In experiments on the decrease of noises caused by industrial machines, it is proved that the performance of Res-Unet was better than other networks, in respect of speech enhancement.
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Pu, Y., Yu, H. (2022). ResUnet: A Fully Convolutional Network for Speech Enhancement in Industrial Robots. In: Fujita, H., Fournier-Viger, P., Ali, M., Wang, Y. (eds) Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. IEA/AIE 2022. Lecture Notes in Computer Science(), vol 13343. Springer, Cham. https://doi.org/10.1007/978-3-031-08530-7_4
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