Abstract
An original algorithm for underdetermined mixing matrix estimation is proposed in this paper, which can estimate the mixing matrix effectively when the number of source signals is unknown. Firstly, a new single source point (SSP) detection algorithm based on transform matrix is proposed, which can effectively detect single source time-frequency (TF) points by using the characteristics of complex ratio and improve the sparsity of source signals. In view of the fact that the number of source signals is unknown, a novel estimation algorithm based on element sorting is proposed, which can significantly improve the estimation accuracy of the number of source signals. Finally, the mixing matrix is estimated by using the cluster center obtained by agglomerative hierarchical clustering (AHC) algorithm. The simulation results show that the proposed algorithm can improve the estimation accuracy of the number of source signals and underdetermined mixing matrix obviously, and the algorithm has higher robustness compared with other algorithms.
Highlights
B LIND source separation (BSS) refers to the process of separating and recovering signals by using only the observed signals when the source signals and the transmission channel are unknown [1]
BSS is defined as underdetermined blind source separation (UBSS) when the number of observed signals is less than the number of source signals [5]
A new single source point (SSP) detection algorithm based on transformation matrix is proposed to improve the sparsity of signals
Summary
B LIND source separation (BSS) refers to the process of separating and recovering signals by using only the observed signals when the source signals and the transmission channel are unknown [1]. An SSP detection algorithm is proposed in reference [15], which can effectively estimate the mixing matrix by using SSPs, but large computational complexity is the main defects. Ye [16] proposed an SSP detection algorithm based on the transformation matrix, and determined the number of source signals by looking for the peak value of the potential function, but the selection of peak value is greatly affected by the noise. We propose a mixing matrix estimation algorithm for UBSS. A new SSP detection algorithm based on transformation matrix is proposed to improve the sparsity of signals. A novel estimation algorithm based on element ordering is proposed to determine the number of source signals. 3 describes the underdetermined mixing matrix estimation algorithm, including SSP detection algorithm, estimation algorithm of the number of source signals, and AHC algorithm.
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