Underdetermined blind source separation method and device based on structured sparse subspace clustering
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An underdetermined blind source separation, subspace technology, applied in character and pattern recognition, pattern recognition in signals, special data processing applications, etc., can solve the problems of weak robustness, insufficient accuracy, and narrow application range. Achieve a wide range of applications, improve the accuracy of signal recovery, and reduce the amount of computation
Pending Publication Date: 2022-01-21
CHINA UNIV OF GEOSCIENCES (WUHAN)
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[0006] One of the main problems solved by the present invention is that the existing underdetermined b
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Embodiment 1
[0086] Embodiment one, such as figure 1 As shown, it is a flowchart of an underdetermined blind source separation method based on structured sparse subspace clustering, which specifically includes the following steps:
[0087] Transform the source signal to the time-frequency domain, use the time-frequency interval windowing method to remove low-energy points, and screen out single source points;
[0088] Using structured sparse subspace clustering to explore the subspace distributed in the single source point, determine the number of signal sources and each cluster to which the single source point belongs, use the improved potential function method to find the cluster center, and estimate the mixing matrix based on the cluster center;
[0089] Based on the mixing matrix, using the spatial projection method based on the estimation of the number of sources combined with the minimum The norm method reconstructs the source signal;
[0090] The reconstructed source signal in th...
Embodiment 2
[0143] In the second embodiment, the speech signal is selected as the source signal, and a specific underdetermined blind source separation method based on structured sparse subspace clustering is described.
[0144] Step 1, select the speech signal, the blind source separation model for underdetermined instantaneous mixing in the time domain can be expressed as:
[0145]
[0146] Among them, s(t)=[s 1 (t), s 2 (t),...,s n (t)] T Indicates n source signals, x(t)=[x 1 (t), x 2 (t),...,x m (t)] T Indicates m-way observation signals. A=[a 1 , a 2 ,...,a n ]∈R M×n (m
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Abstract
The invention provides an underdetermined blind source separation method and device based on structured sparse subspace clustering, and the method comprises the steps of converting a time domain signal to a time-frequency domain through short-time Fourier transform, and screening a single source point with a high energy value through a time-frequency interval windowing method based on the short-time stability of the signal; then, adopting a combined hybrid matrix estimation method of structured sparse subspace clustering and an improved potential function, where the structured sparse subspace clustering can determine the number of source signals and obtain data classification, and the improved potential function method can accurately determine a hybrid matrix vector and estimate a hybrid matrix; and then, reconstructing a source signal by adopting a space projection method based on source number estimation in combination with a minimum l1 norm method, and finally, recovering a time-frequency domain reconstruction signal to a time domain by adopting short-time inverse Fourier transform. According to the invention, the number of the source signals can be visually determined, high precision and high robustness are achieved, and the source signals can be accurately reconstructed from the observation signals.
Description
technical field [0001] The invention relates to the technical field of signal processing, in particular to an underdetermined blind source separation method and device based on structured sparse subspace clustering. Background technique [0002] Blind source separation refers to a class of problems in which the source signal and its mixing method are unknown, and only the source signal is separated from the mixed signal observed by the sensor. Since this problem widely exists in practical engineering applications, it has become a research hotspot in recent years. It is often used in speech signal processing, biomedical signal processing, image processing, fault diagnosis and other fields. [0003] In practice, due to the complexity of the engineering environment and the consideration of cost control, the number of observation sensors is often known, and the number of observed source signals is usually unknown and more than the number of observation signals. In this case, th...
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