摘要
针对助听器回声路径快速变化下易产生啸叫的问题,本文提出一种变步长标准最小均方差-陷波器(Variable Step Normalized least mean square-Notch Filter,VSN-NF)算法。在回声路径相对稳定时,提出一种基于状态分类的变步长标准最小均方差算法来估计回声信号。算法根据滤波器系数能量的长时平均值和短时平均值,将滤波器当前状态分为收敛态、过渡态与稳态,并根据不同状态选择不同的步长。在路径突然变化并产生啸叫时,算法通过关闭变步长NLMS算法来稳定啸叫频点,然后基于ZoomFFT算法动态生成陷波器来进行啸叫抑制;当啸叫抑制后,再开启变步长NLMS进行回声估计。针对易产生多频点啸叫的回声路径,VSN-NF算法还引入不同频带的两个陷波器来进行双频点啸叫抑制。同其它助听器回声抵消算法的对比实验显示,VSN-NF算法的回波抵消性能最好,尤其具有快速啸叫抑制能力。此外,算法生成的语音质量较高,实时性能好,适合于像助听器类的低功耗、小体积产品。
When the feedback path of the hearing aid is suddenly changed, the whistle easily exists. To solve this problem, one variable step normalized least mean square - notch filter (VSN-NF) algorithm is proposed. When the echo path is relatively stable, the echo signal is estimated by using the variable step normalized least mean square (VSNLMS) algorithm based on state classification and subtracted from the input signal. The normalized distance between short term average and long term average of the filter coefficients is used to classify the filter state, which is categorized into convergent state, transitional state and steady state. Different step sizes are employed in different state. When the hearing aid whistles, the VSNLMS algorithm is closed to stabilize the whistle frequency. Then, the notch filter for the whistle frequency is generated dynamically to restrain the whistle. Finally, when the whistle is suppressed, the VSNLMS algorithm is opened again. In addition, two notch filters for the different band are introduced to restrain the dual- band whistle for the echo path which is easy to whistle at the multiple frequencies. Compared with the other feedback cancellation algorithms for the hearing aid, results show that the comprehensive performance of VSN-NF is the best and the algorithm can quickly suppress the whistle. Furthermore, the quality of speech generated by the algorithm is higher and the operation time is shorter, which is suitable for the low-power consumption and small volume products such as hearing aids.
出处
《声学学报》
EI
CSCD
北大核心
2016年第2期249-259,共11页
Acta Acustica
基金
国家自然科学基金(61301219
61375028
61301295)
江苏省自然科学基金(BK20130241)
南京工程学院高层次引进人才科研启动基金(YKJ201526)资助