A Zynq-Based Platform With Conditional- Reconfigurable Complex-Valued Neural Network for Specific Emitter Identification

Gan, Jiayan; Li, Qiang; Shao, Huaizong; Wen, Zhongyi; Yang, Tao; Pan, Ye; Sun, Guomin

doi:10.1109/tim.2024.3391818

Cited by 2 publications

(1 citation statement)

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“…In recent years, CVNN has been widely used in signal processing and other fields [39][40][41][42], such as speech enhancement and image segmentation. A speech signal, as a wave signal, has a phase component that represents the time difference and an amplitude that represents the energy of the wave [43].…”

Section: Related Workmentioning

confidence: 99%

Robust DOA Estimation Using Multi-Scale Fusion Network with Attention Mask

Yan,

Huang

2024

Applied Sciences

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To overcome the limitations of traditional methods in reverberant and noisy environments, a robust multi-scale fusion neural network with attention mask is designed to improve direction-of-arrival (DOA) estimation accuracy for acoustic sources. It combines the benefits of deep learning and complex-valued operations to effectively deal with the interference of reverberation and noise in speech signals. The unique properties of complex-valued signals are exploited to fully capture inherent features and rich information is preserved in the complex field. An attention mask module is designed to generate distinct masks for selectively focusing and masking based on the input. After that, the multi-scale fusion block efficiently captures multi-scale spatial features by stacking complex-valued convolutional layers with small size kernels, and reduces the module complexity through special branching operations. Experimental results demonstrate that the model achieves significant improvements over other methods for speaker localization in reverberant and noisy environments. It provides a new solution for DOA estimation for acoustic sources in different scenarios, which has significant theoretical and practical implications.

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