Abstract Acoustic feedback is a frequently encountered problem in assistive listening devices (ALDs). Feedback paths in ALDs are typically sparse in nature and sparsity aware adaptive feedback cancellers can improve perceived audio quality under such scenarios. In an endeavour to improve the feedback canceller performance, a decorrelated polynomial zero attraction (DPZA) normalized least mean square (NLMS) feedback canceller is proposed in this paper. DPZA-NLMS algorithm is seen to have higher computational complexity. Hence, in an attempt to reduce computational complexity, a decorrelated l 0 -NLMS (D- l 0 -NLMS) and a decorrelated Versoria zero attraction NLMS (DVZA-NLMS) based feedback canceller are also proposed. Feedback canceller performance in terms of convergence and tracking performance as well as speech/audio quality and speech intelligibility is compared. In addition, computational complexity and memory requirements of the algorithms are also compared thus providing a hearing aid designer with better trade off choices between computational requirements and feedback cancellation performance.

中文翻译：

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一类用于助听器的基于零吸引的稀疏自适应反馈消除器的设计

摘要 声反馈是助听设备 (ALD) 中经常遇到的问题。ALD 中的反馈路径本质上通常是稀疏的，稀疏感知自适应反馈消除器可以在这种情况下提高感知音频质量。为了提高反馈消除器的性能，本文提出了一种去相关多项式零吸引（DPZA）归一化最小均方（NLMS）反馈消除器。DPZA-NLMS 算法被认为具有更高的计算复杂度。因此，为了降低计算复杂度，还提出了基于去相关的l 0 -NLMS（D-l 0 -NLMS）和去相关的Versoria零吸引力NLMS（DVZA-NLMS）的反馈消除器。比较了在收敛和跟踪性能以及语音/音频质量和语音清晰度方面的反馈消除器性能。此外，还比较了算法的计算复杂度和存储器要求，从而为助听器设计者提供了计算要求和反馈消除性能之间更好的折衷选择。