基于L0-IPNLMS的低复杂度数字助听器回声消除算法

doi:10.16337/j.1004-9037.2021.05.009

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基于L0-IPNLMS的低复杂度数字助听器回声消除算法
DOI:
                        10.16337/j.1004-9037.2021.05.009
                    
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作者单位:1.南京邮电大学通信与信息工程学院，南京 210003;2.南京邮电大学江苏省通信与网络技术工程研究中心，南京 210003
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基金项目:国家自然科学基金(61771258)资助项目；江苏省高校自然科学研究重大项目（13KJA510003）资助项目。

Low-Complexity Echo Cancellation Algorithm Based on L0-IPNLMS for Hearing Aids

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1.College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;2.Jiangsu Provincial Engineering Research Center of Telecommunications and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

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摘要:

针对数字助听器中回声消除算法计算复杂度高的问题，提出了一种基于集员滤波（Set membership filtering， SMF）理论的变步长基于L0范数的改进比例归一化最小均方误差算法（L0-norm constrained improved proportional NLMS， L0-IPNLMS）算法。该算法将集员滤波的时变步长引入到L0-IPNLMS算法中，不仅提高了系统的收敛特性，而且充分利用了集员滤波理论的数据选择更新特性，在误差幅度有界的前提下进行滤波器系数的更新，减少了不必要的迭代次数，降低了数字助听器的功耗。仿真实验表明，与L0-IPNLMS算法相比，结合集员滤波和L0范数的改进比例归一化最小均方误差算法（L0-Norm constrained improved proportional NLMS based on set membership filtering theory， SM-L0-IPNLMS）算法在保留稀疏性的同时，计算复杂度降低了15.3%，在以随机信号和真实语音作为输入信号时收敛速度分别提高了28%、32.8%，失调量分别降低了1 dB、3 dB，均方误差分别降低了0.66 dB和1.68 dB，回声损失值则分别提升了0.7 dB和1.79 dB。此外，算法在低信噪比的输入条件下也具有较强的鲁棒性。

Abstract:

An L0-norm constrained improved proportional normalized least-mean-square （L0-IPNLMS） algorithm based on set membership filtering （SMF） theory （SM-L0-IPNLMS） is proposed to effectively reduce the computational complexity of echo cancellation algorithms in digital hearing aids. The variable step size theory of set membership （SM） is introduced into the L0-IPNLMS algorithm to achieve a faster convergence speed in the proposed algorithm. Moreover， by updating the filter coefficients selectively under the bounded error margin， unnecessary iterations are reduced and then the power consumption of the digital hearing aids are decreased. Experiments demonstrate that compared to the L0-IPNLMS algorithm， the computation of the new algorithm is reduced by 15.3%. In the situation that random signal and real speech are input respectively， the convergence speed is improved by 28% and 32.8%， the misalignment is reduced by 1 dB and 3 dB， the mean square error is reduced by 0.66 dB and 1.68 dB， the echo loss enhancement is improved by 0.7 dB and 1.79 dB correspondingly. Furthermore， the SM-L0-IPNLMS algorithm is greatly robust for the input conditions of low SNRs.