Distributed Combined Acoustic Echo Cancellation and Noise Reduction in Wireless Acoustic Sensor and Actuator Networks
Distributed Combined Acoustic Echo Cancellation and Noise Reduction in Wireless Acoustic Sensor and Actuator Networks
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DOI：
10.1109/taslp.2022.3140548
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发表时间：
2022-01-01
影响因子：
5.4
通讯作者：
Moonen, Marc
中科院分区：
文献类型：
--
作者：
Ruiz, Santiago;van Waterschoot, Toon;Moonen, Marc
提出了一种用于无线声传感器和执行器网络(WASAN)中的组合回声抵消(AEC)和降噪(NR)的分布式算法，其中每个节点可以具有多个麦克风和多个扬声器，并且期望的信号是语音信号。该算法采用集中式AEC和NR相结合的算法，即多通道维纳滤波(MWF)，将回波信号视为背景噪声信号，扬声器信号作为附加输入信号。通过包括先验知识(PK)，即扬声器信号不包含任何期望的信号分量，获得了替换的集中式级联算法(PK-MWF)，其中首先是AEC级，然后是具有较低计算复杂度的基于MWF的NR级联。然后由MWF和PK-MWF算法分别得到分布式算法，即基于广义特征值分解(GEVD)的分布式自适应节点特定信号估计(DANSE)算法和PK-GEVD-DANSE算法。在前者中，每个节点执行降维综合AEC和NR算法，并且只向其他节点广播一个融合信号(而不是其所有信号)。在PK-GEVD-DANSE算法中，每个节点执行降维级联AEC和NR算法，并且只向其他节点广播2个融合信号(而不是其所有信号)。在收敛时，分布式算法获得与相应的集中式集成(MWF)和集中式级联(PK-MWF)算法相同的性能。然而，可以观察到，PK-GEVD-DANSE算法中的通信成本也可以降低，其中每个节点随后仅向其他节点广播1个融合信号(而不是2个信号)。由此得到的算法称为剪枝PK-GEVD-DANSE(PPK-GEVD-DANSE)算法，它有效地结合了可能的最低通信成本(与GEVD-DANSE算法中的一样低)和每个节点的可能最低的计算复杂性(进一步降低了PK-GEVD-DANSE的计算复杂性)，属于本文所考虑的算法类别。
The paper presents distributed algorithms for combined acoustic echo cancellation (AEC) and noise reduction (NR) in a wireless acoustic sensor and actuator network (WASAN) where each node may have multiple microphones and multiple loudspeakers, and where the desired signal is a speech signal. A centralized integrated AEC and NR algorithm, i.e., multichannel Wiener filter (MWF), is used as starting point where echo signals are viewed as background noise signals and loudspeaker signals are used as additional input signals to the algorithm. By including prior knowledge (PK), namely that the loudspeaker signals do not contain any desired signal component, an alternative centralized cascade algorithm (PK-MWF) is obtained with an AEC stage first followed by an MWF-based NR stage which has a lower computational complexity. Distributed algorithms can then be obtained from the MWF and PK-MWF algorithm, i.e., the generalized eigenvalue decomposition (GEVD)-based distributed adaptive node-specific signal estimation (DANSE) and PK-GEVD-DANSE algorithm, respectively. In the former, each node performs a reduced dimensional integrated AEC and NR algorithm and broadcasts only 1 fused signal (instead of all its signals) to the other nodes. In the PK-GEVD-DANSE algorithm, each node performs a reduced dimensional cascade AEC and NR algorithm and broadcasts only 2 fused signals (instead of all its signals) to the other nodes. The distributed algorithms achieve the same performance, upon convergence, as the corresponding centralized integrated (MWF) and centralized cascade (PK-MWF) algorithm. It is observed, however, that the communication cost in the PK-GEVD-DANSE algorithm can also be reduced, where each node then broadcasts only 1 fused signal (instead of 2 signals) to the other nodes. The resulting algorithm, referred to as the pruned PK-GEVD-DANSE (pPK-GEVD-DANSE) algorithm, then effectively combines the lowest possible communication cost (as low as in the GEVD-DANSE algorithm) with a lowest possible computational complexity in each node (further reduced from the PK-GEVD-DANSE computational complexity), within the class of algorithms considered in this paper.