Abstract:For the problem that the existing methods for noise type recognition and intensity estimation all focus on single noises, and cannot estimate the intensity of source noises in the mixed noises, a K-Nearest Neighbors (KNN) algorithm with distance threshold was proposed to recognize the single and mixed noises, and estimate the intensity of source noises in the mixed noises by combining the recognition results of mixed noises and the reconstruction of noise bases. Firstly, the data distribution in frequency domain was used as feature vector. Then the signals were identified by the noise type recognition algorithm, and the frequency domain cosine distance between reconstructed noise and real noise was adopted as the optimal evaluation criterion in the process of reconstruction of noise bases. Finally, the intensity of source noises was estimated. The experimental results on two test databases indicate that, the proposed algorithm has the average accuracy of noise type identification as high as 98.135%, and the error rate of intensity estimation of mixed noise of 20.96%. The results verify the accuracy and generalization of noise type recognition algorithm as well as the feasibility of mixed noise intensity estimation algorithm, and this method provides a new idea for the mixed noise intensity estimation. The information of mixed noise type and intensity obtained by this method contributes to the determination of denoising methods and parameters, and improves the denoising efficiency.
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