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The technique thus provides improved robustness to noise without sacrificing performance in clean conditions
The LRTF-based method can effectively separate the LR clean image from sparse noise.
Experimental results demonstrate that the proposed method can disentangle speaker and noise attributes even if they are correlated in the training data, and can be used to consistently synthesize clean speech for all speakers.
Evaluation results show that the new scream detection solution works well for clean, +20, +10 dB SNR levels, with performance declining as SNR decreases to -20dB across a number of the noise sources considered.
In addition, we introduce two novel objective measures and further show the efficiency of our algorithm in maintaining the clean speech while reducing the noise as much as possible.
More importantly, the proposed framework is robust to noise, as improvements are maintained when the system is trained on clean data.
In this paper, we propose a novel robust principal component analysis approach for mixed noise removal by fully identifying the intrinsic structures of the mixed noise and clean HSI.
The results show that the proposed technique outperforms the white noise based multicondition and the clean-speech training approaches.