Blind identification-blind equalization for Finite Impulse Response (FIR) Multiple Input-Multiple Output (MIMO) channels can be reformulated as the problem of blind sources separation. It has been shown that blind ide...Blind identification-blind equalization for Finite Impulse Response (FIR) Multiple Input-Multiple Output (MIMO) channels can be reformulated as the problem of blind sources separation. It has been shown that blind identification via decorrelating sub-channels method could recover the input sources. The Blind Identification via Decorrelating Sub-channels(BIDS)algorithm first constructs a set of decorrelators, which decorrelate the output signals of subchannels, and then estimates the channel matrix using the transfer functions of the decorrelators and finally recovers the input signal using the estimated channel matrix. In this paper, a new approximation of the input source for FIR-MIMO channels based on the maximum likelihood source separation method is proposed. The proposed method outperforms BIDS in the presence of additive white Gaussian noise.展开更多
A novel blind source separation （BSS） algorithm based on the combination of negentropy and signal noise ratio （SNR） is presented to solve the deficiency of the traditional independent component analysis （ICA） al...A novel blind source separation （BSS） algorithm based on the combination of negentropy and signal noise ratio （SNR） is presented to solve the deficiency of the traditional independent component analysis （ICA） algorithm after the introduction of the principle and algorithm of ICA. The main formulas in the novel algorithm are elaborated and the idiographic steps of the algorithm are given. Then the computer simulation is used to test the performance of this algorithm. Both the traditional FastlCA algorithm and the novel ICA algorithm are applied to separate mixed signal data. Experiment results show the novel method has a better performance in separating signals than the traditional FastlCA algorithm based on negentropy. The novel algorithm could estimate the source signals from the mixed signals more precisely.展开更多
In this paper, a speech signal recovery algorithm is presented for a personalized voice command automatic recognition system in vehicle and restaurant environments. This novel algorithm is able to separate a mixed spe...In this paper, a speech signal recovery algorithm is presented for a personalized voice command automatic recognition system in vehicle and restaurant environments. This novel algorithm is able to separate a mixed speech source from multiple speakers, detect presence/absence of speakers by tracking the higher magnitude portion of speech power spectrum and adaptively suppress noises. An automatic speech recognition (ASR) process to deal with the multi-speaker task is designed and implemented. Evaluation tests have been carried out by using the speech da- tabase NOIZEUS and the experimental results show that the proposed algorithm achieves impressive performance improvements.展开更多
The strong noise produced by the leakage of electricity from marine seismic streamers is often received with seismic signals during marine seismic exploration. Traditional denoising methods show unsatisfactory effects...The strong noise produced by the leakage of electricity from marine seismic streamers is often received with seismic signals during marine seismic exploration. Traditional denoising methods show unsatisfactory effects when eliminating strong noise of this kind. Assuming that the strong noise signals have the same statistical properties, a blind source separation （BSS） algorithm is proposed in this paper that results in a new denoising algorithm based on the constrained multi-user kurtosis （MUK） optimization criterion. This method can separate strong noise that shares the same statistical properties as the seismic data records and then eliminate them. Theoretical and field data processing all show that the denoising algorithm, based on multi-user kurtosis optimization criterion, is valid for eliminating the strong noise which is produced by the leakage of electricity from the marine seismic streamer so as to preserve more effective signals and increase the signal-noise ratio. This method is feasible and widely applicable.展开更多
针对欠定盲源分离中混合矩阵估计精度不佳的问题,本文提出了一种结合带噪声的基于密度的空间聚类(combining density-based spatial clustering of application with noise,DBSCAN)和概率密度估计的混合矩阵估计算法。首先,通过向量转...针对欠定盲源分离中混合矩阵估计精度不佳的问题,本文提出了一种结合带噪声的基于密度的空间聚类(combining density-based spatial clustering of application with noise,DBSCAN)和概率密度估计的混合矩阵估计算法。首先,通过向量转换方式获得单声源时频点检测准则,并基于此准则从混合信号中检测出单声源点。其次,利用基于密度的空间聚类算法对单声源点进行聚类,由此估计出声源个数以及各类别所属的单声源点。再次,利用概率密度估计获得各类别的聚类中心,并构成混合矩阵。所提混合矩阵估计方法不需要提前设定声源个数,并且避免了由于数据分布不均所造成的聚类效果差的问题。最后,采用压缩感知技术实现源信号恢复,从而从混合信号中分离出各个声源信号。实验结果表明,本文所提的混合矩阵估计方法在声源个数未知的情况下,能够准确估计出混合矩阵;并且分离出的信号具有较高的质量。展开更多
基金Supported by the National Natural Science Foundation of China (No.60172048)
文摘Blind identification-blind equalization for Finite Impulse Response (FIR) Multiple Input-Multiple Output (MIMO) channels can be reformulated as the problem of blind sources separation. It has been shown that blind identification via decorrelating sub-channels method could recover the input sources. The Blind Identification via Decorrelating Sub-channels(BIDS)algorithm first constructs a set of decorrelators, which decorrelate the output signals of subchannels, and then estimates the channel matrix using the transfer functions of the decorrelators and finally recovers the input signal using the estimated channel matrix. In this paper, a new approximation of the input source for FIR-MIMO channels based on the maximum likelihood source separation method is proposed. The proposed method outperforms BIDS in the presence of additive white Gaussian noise.
文摘A novel blind source separation （BSS） algorithm based on the combination of negentropy and signal noise ratio （SNR） is presented to solve the deficiency of the traditional independent component analysis （ICA） algorithm after the introduction of the principle and algorithm of ICA. The main formulas in the novel algorithm are elaborated and the idiographic steps of the algorithm are given. Then the computer simulation is used to test the performance of this algorithm. Both the traditional FastlCA algorithm and the novel ICA algorithm are applied to separate mixed signal data. Experiment results show the novel method has a better performance in separating signals than the traditional FastlCA algorithm based on negentropy. The novel algorithm could estimate the source signals from the mixed signals more precisely.
文摘In this paper, a speech signal recovery algorithm is presented for a personalized voice command automatic recognition system in vehicle and restaurant environments. This novel algorithm is able to separate a mixed speech source from multiple speakers, detect presence/absence of speakers by tracking the higher magnitude portion of speech power spectrum and adaptively suppress noises. An automatic speech recognition (ASR) process to deal with the multi-speaker task is designed and implemented. Evaluation tests have been carried out by using the speech da- tabase NOIZEUS and the experimental results show that the proposed algorithm achieves impressive performance improvements.
基金supported by the National Natural Science Foundation of China(No. 41176077)the State Oceanic Administration Young Marine Science Foundation(No. 2013702)
文摘The strong noise produced by the leakage of electricity from marine seismic streamers is often received with seismic signals during marine seismic exploration. Traditional denoising methods show unsatisfactory effects when eliminating strong noise of this kind. Assuming that the strong noise signals have the same statistical properties, a blind source separation （BSS） algorithm is proposed in this paper that results in a new denoising algorithm based on the constrained multi-user kurtosis （MUK） optimization criterion. This method can separate strong noise that shares the same statistical properties as the seismic data records and then eliminate them. Theoretical and field data processing all show that the denoising algorithm, based on multi-user kurtosis optimization criterion, is valid for eliminating the strong noise which is produced by the leakage of electricity from the marine seismic streamer so as to preserve more effective signals and increase the signal-noise ratio. This method is feasible and widely applicable.
文摘针对欠定盲源分离中混合矩阵估计精度不佳的问题,本文提出了一种结合带噪声的基于密度的空间聚类(combining density-based spatial clustering of application with noise,DBSCAN)和概率密度估计的混合矩阵估计算法。首先,通过向量转换方式获得单声源时频点检测准则,并基于此准则从混合信号中检测出单声源点。其次,利用基于密度的空间聚类算法对单声源点进行聚类,由此估计出声源个数以及各类别所属的单声源点。再次,利用概率密度估计获得各类别的聚类中心,并构成混合矩阵。所提混合矩阵估计方法不需要提前设定声源个数,并且避免了由于数据分布不均所造成的聚类效果差的问题。最后,采用压缩感知技术实现源信号恢复,从而从混合信号中分离出各个声源信号。实验结果表明,本文所提的混合矩阵估计方法在声源个数未知的情况下,能够准确估计出混合矩阵;并且分离出的信号具有较高的质量。
