多种去相关方法联合提升声反馈消除效果
The Combination of Several Decorrelation Methods to Improve Acoustic Feedback Cancellation
- 在频域卡尔曼滤波器中引入时延、预测等多重去相关技术
- 组合使用所有方法后系统性能显著优于单一改进
- 适合语音处理与音频系统研发人员参考
本文通过整合多种去相关方法，扩展了声反馈消除系统。基线系统基于多延迟结构的频域卡尔曼滤波器。所提改进包括可变时延线、预测、失真补偿和简化混响模型，每项均经分析并定义实用参数范围。尽管已有研究多聚焦单一改进（如预测），本文证明各项独立贡献性能提升，且全部结合后实现更优效果。评估采用公开数据集，通过系统距离指标与客观语音质量度量PSEQ进行验证。
原文摘要 · Abstract (English)
This paper extends an acoustic feedback cancellation system by incorporating multiple decorrelation methods. The baseline system is based on a frequency-domain Kalman filter implemented in a multi-delay structure. The proposed extensions include a variable time delay line, prediction, distortion compensation, and a simplified reverberation model. Each extension is analyzed, and a practical parameter range is defined. While existing literature often focuses on a single extension, such as prediction, to describe an optimal system, this work demonstrates that each individual extension contributes to performance improvements. Furthermore, the combination of all proposed extensions results in a superior system. The evaluation is conducted using publicly available datasets, with performance assessed through system distance metrics and the objective speech quality measure PSEQ.
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