Frequency domain blind separation method and system with low complexity
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A low-complexity, blind separation technology, applied in the field of low-complexity frequency-domain blind separation methods and systems, can solve problems such as weakening inter-frequency correlations
Active Publication Date: 2019-07-12
INST OF ACOUSTICS CHINESE ACAD OF SCI
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How to solve the convolutional mixture blind separation problem is a challenging problem
In addition, if the mixed signal is not completely separated in the early separation process, the inter-frequency correlation at this time will also be weakened.
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Embodiment 1
[0093] Embodiment 1 of the present invention provides a low-complexity frequency-domain blind separation system, and the system specifically includes:
[0094] Such as figure 1 as shown, figure 1It is a block diagram of a signal mixing and separation system, including a mixing system module 101 and a separation system module 102 . Each sound source signal reaches the microphone through different transmission paths, and the microphone receives the mixed observation signal, and then passes through the separation system to obtain the estimation of the sound source.
[0095] The hybrid system module 101 is to combine N sound source signals s 1 (t),s 2 (t),...,s N (t) and room impulse response h ji (t) (representing the FIR impulse response of length P between the i-th sound source and the j-th microphone) is convoluted and combined to obtain M observation signals x 1 (t),x 2 (t),...,x M (t).
[0096] The separation system module 102 is to observe the signal x through the ...
Embodiment 2
[0150] Embodiment 2 of the present invention provides a low-complexity frequency-domain blind separation method, which specifically includes:
[0151] Step 1) performing short-time Fourier transform on the mixed signal collected by the microphone array to obtain a frequency domain signal, and independently using the complex ICA algorithm to separate the sound source at each frequency point; obtaining the separated signal of each frequency point;
[0152] Step 1-1) Receive observation signal x from M microphones j (t) After the short-time Fourier transform with a window length of Q point, the frequency domain signal X is obtained j (l,f), t is the time; 1≤j≤M, l is the time index, 1≤l≤B, B represents the total number of frames processed by the mixed data frame; f is the frequency index, f s is the sampling frequency; x(l,f)=[X 1 (l,f),X 2 (l,f),...,X M (l,f)] T is the observed signal frequency domain vector;
[0153] Step 1-2) Use the frequency domain ICA algorithm to i...
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Abstract
The invention discloses a frequency domain blind separation method and a frequency domain blind separation system with low complexity. The method comprises the following steps: acquiring frequency domain separation signals collected by a microphone array; carrying out correlation analysis on the separation signals of the current frequency point and the pervious frequency point, thus completing thelocal sorting of the separation signal of each frequency point; carrying out correlation analysis on the separation signal of each frequency point after local sorting and the global central point, thus completing the global sorting of the separation signal of each frequency point; carrying out amplitude adjustment on the separation signals after global sorting; and carrying out Fourier transformon the adjusted separation signals, thus obtaining the separation signals of the time domain. With the method combining local sorting with global sorting provided by the invention, the accuracy and robustness of separation can be improved, meanwhile, the iteration times of the sorting process is reduced, and the computation complexity is relatively low.
Description
technical field [0001] The invention relates to the field of blind signal processing, in particular to a low-complexity frequency-domain blind separation method and system. Background technique [0002] Blind source separation is to separate or estimate the waveform of the source signal from the output signal of the system when the characteristics of the transmission channel are unknown, the input information is unknown, or there is only a small amount of prior information. According to the signal mixing method, blind source separation can be divided into two cases: instantaneous mixing and convolution mixing. The former is just a simple linear superposition. So far, the linear instantaneous mixing blind separation algorithm is the most mature type of blind separation algorithm; and the volume The product mixture model needs to consider the impulse response between the source and the microphone. In many practical applications, such as the separation of acoustic signals in a...
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