AI 中文总结
研究针对传统助听器语音增强算法在复杂场景中特征提取不可靠的问题，提出基于输出特性确定声音处理系统设置的范式，采用基于输出的MPDR波束形成器系统，实验证明该系统在多指标上优于传统MVDR基线。
AI 中文摘要
传统上，助听器语音增强（SE）算法依靠基于输入的特征估计（通常由语音活动检测器（VAD）得出）来配置波束形成器。然而，在用户最需要帮助的具有挑战性的声学场景中，从嘈杂麦克风信号中提取的特征可能变得不可靠。我们引入了一种新颖的范式，其中通过评估其输出特性来确定声音处理系统的设置。为了证明这一想法，我们采用了一种基于输出的系统，该系统在一组最小功率无失真响应（MPDR）波束形成器中进行选择。尽管由于对指向误差敏感，MPDR波束形成器通常被避免使用，但我们表明它们在基于输出的框架内变得有效。我们将所提出的系统与传统的基于输入的最小方差无失真响应（MVDR）基线进行比较。实验结果表明，在SNR、ESTOI和PESQ方面，所提出的系统始终优于MVDR基线，尤其是在低SNR时。
英文摘要
Traditionally, hearing-aid speech enhancement (SE) algorithms rely on input-based feature estimation, often derived by a voice activity detector (VAD), to configure beamformers. Yet features extracted from noisy microphone signals can become unreliable in challenging acoustic scenes where users most need help. We introduce a novel paradigm in which the settings of a sound processing system are determined by evaluating characteristics of its output. To demonstrate this idea, we employ an output-based system that selects among a set of minimum power distortionless response (MPDR) beamformers. Although MPDR beamformers are typically avoided due to their sensitivity to steering errors, we show that they become effective within an output-based framework. We compare the proposed system to a conventional input-based minimum variance distortionless response (MVDR) baseline. Experimental results show that the proposed system consistently outperforms the MVDR baseline, particularly at low SNRs, in terms of SNR, ESTOI and PESQ.
CommentsAccepted at the International Workshop on Acoustic Signal Enhancement (IWAENC) 2026
