科研之友
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摘要
Symmetric adaptive decorrelation (SAD) is a semi-blind method of separating convolutely mixed signals. While it has restrictions on the physical layout of the demixing equipment, it is better suited for some applications (e.g., live sound mixing) as no post-processing is required to ascertain which output corresponds with which source. Since SAD is based on the least mean squares algorithm, it can be modified to perform the bulk of the processing in the frequency domain. This makes it more efficient for larger filter sizes and/or larger number of sources but renders it unsuitable for real-time applications as there is a lag between the output and the input. In this paper, we propose a hybrid approach that does not suffer from the lag of the frequency domain approach. While the proposed algorithm is slightly less computationally efferent than the pure frequency domain algorithm, it is significantly more efficient than the time domain approach. A comparison of the frequency domain and hybrid algorithms shows that both achieve separation equivalent to the time domain algorithm in a real-world environment.
- 出版日期2017-7
