Speech enhancement with a GSC-like structure employing sparse coding  

作　　者：Li-chun YANG Yun-tao QIAN 

机构地区：[1]College of Computer Science and Technology, Zhejiang University [2]Intelligent Control Research Institute, Zhejiang Wanli University

出　　处：《Journal of Zhejiang University-Science C(Computers and Electronics)》2014年第12期1154-1163,共10页浙江大学学报C辑（计算机与电子（英文版）

基　　金：Project supported by the National Basic Research Program(973)of China(No.2012CB316400);the National NaturalScience Foundation of China(No.61171151)

摘　　要：Speech communication is often influenced by various types of interfering signals. To improve the quality of the desired signal, a generalized sidelobe canceller(GSC), which uses a reference signal to estimate the interfering signal, is attracting attention of researchers. However, the interference suppression of GSC is limited since a little residual desired signal leaks into the reference signal. To overcome this problem, we use sparse coding to suppress the residual desired signal while preserving the reference signal. Sparse coding with the learned dictionary is usually used to reconstruct the desired signal. As the training samples of a desired signal for dictionary learning are not observable in the real environment, the reconstructed desired signal may contain a lot of residual interfering signal. In contrast,the training samples of the interfering signal during the absence of the desired signal for interferer dictionary learning can be achieved through voice activity detection(VAD). Since the reference signal of an interfering signal is coherent to the interferer dictionary, it can be well restructured by sparse coding, while the residual desired signal will be removed. The performance of GSC will be improved since the estimate of the interfering signal with the proposed reference signal is more accurate than ever. Simulation and experiments on a real acoustic environment show that our proposed method is effective in suppressing interfering signals.Speech comnmnication is often influenced by various types of interfering signals. To improve the quality of the desired signal, a generalized sidelobe canceller （GSC）, which uses a reference signal to estimate the interfering signal, is attracting attention of researchers. However, the interference suppression of GSC is limited since a little residual desired signal leaks into the reference signal. To overcome this problem, we use sparse coding to suppress the residual desired signal while preserving the reference signal. Sparse coding with the learned dictionary is usually used to reconstruct the desired signal. As the training samples of a desired signal for dictionary learning are not observable in the real environment, the reconstructed desired signal may contain a lot of residual interfering signal. In contrast, the training samples of the interfering signal during the absence of the desired signal for interferer dictionary learning can be achieved through voice activity detection （VAD）. Since the reference signal of an interfering signal is coherent to the interferer dictionary, it can be well restructured by sparse coding, while the residual desired signal will be removed. The performance of GSC will be improved since the estimate of the interfering signal with the proposed reference signal is more accurate than ever. Simulation and experiments on a real acoustic environment show that our proposed method is effective in suppressing interfering signals.

关 键 词：Generalized sidelobe canceller Speech enhancement Voice activity detection Dictionary learning Sparse coding 

分 类 号：TN912.35[电子电信—通信与信息系统]