Abstract
Deep spatially selective filters achieve high-quality enhancement with real-time capable architectures for stationary speakers of known directions. To retain this level of performance in dynamic scenarios when only the speakers' initial directions are given, accurate, yet computationally lightweight tracking algorithms become necessary. Assuming a frame-wise causal processing style, temporal feedback allows for leveraging the enhanced speech signal to improve tracking performance. In this work, we investigate strategies to incorporate the enhanced signal into lightweight tracking algorithms and autoregressively guide deep spatial filters. Our proposed Bayesian tracking algorithms are compatible with arbitrary deep spatial filters. To increase the realism of simulated trajectories during development and evaluation, we propose and publish a novel dataset based on the social force model. Results validate that the autoregressive incorporation significantly improves the accuracy of our Bayesian trackers, resulting in superior enhancement with none or only negligibly increased computational overhead. Real-world recordings complement these findings and demonstrate the generalizability of our methods to unseen, challenging acoustic conditions.
Abstract (translated)
深度空间选择性滤波器通过适用于静态声源且已知方向的实时架构实现高质量增强。当仅已知声源初始方向时，为在动态场景中保持同等性能，需要精确且计算轻量的跟踪算法。假设采用逐帧因果处理模式，时间反馈可利用增强后的语音信号来提升跟踪性能。本研究探索了将增强信号融入轻量跟踪算法并自回归引导深度空间滤波器的策略。所提出的贝叶斯跟踪算法兼容任意深度空间滤波器。为提升开发与评估中模拟轨迹的真实性，我们基于社会力模型提出并发布了一个新数据集。结果验证了自回归融合显著提高了贝叶斯跟踪器的精度，从而在几乎不增加计算开销的情况下实现更优的增强效果。真实世界录音补充了这些发现，并展示了该方法在未见且具有挑战性的声学条件中的泛化能力。
URL
https://arxiv.org/abs/2603.23723
