基于深度学习的混响环境中空间频率提示生成式固定滤波器有源噪声控制
Spatial-Frequency Cued Generative Fixed-Filter Active Noise Control Based on Deep Learning in Reverberant Environments
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中文总结 AI 辅助
本文针对混响环境中GFANC未考虑噪声源空间信息的问题，提出SF-GFANC方法，设计CRNN估计空间和频率提示来指导控制滤波器生成，经理论分析和实验验证，该方法对未知环境和噪声类型鲁棒，性能优于代表性算法。
中文摘要 AI 辅助
生成式固定滤波器有源噪声控制（GFANC）通过子控制滤波器组合有效衰减具有不同频率特性的噪声。然而，它未纳入噪声源的空间信息，限制了其性能，尤其是在混响环境中。为解决此限制，本文提出一种新颖的空间频率提示GFANC（SF-GFANC）方法，利用噪声源的三维空间和频率信息。具体而言，设计多任务卷积循环神经网络（CRNN）估计源距离、仰角和方位角作为空间提示，同时预测子控制滤波器的组合权重作为频率提示。这些空间频率提示共同指导生成合适的控制滤波器。此外，对混响环境中最优控制滤波器进行了理论分析。使用模拟和实测声路径的评估表明，CRNN对未见声学环境和噪声类型具有鲁棒性。结果还证实，在处理混响环境中不同三维位置和频率特性的噪声源时，SF-GFANC优于代表性的有源噪声控制算法。
英文摘要
Generative fixed-filter active noise control (GFANC) effectively attenuates noise with diverse frequency characteristics through the combination of sub control filters. However, it does not incorporate the spatial information of the noise source, which limits its performance, particularly in reverberant environments. To address this limitation, this paper proposes a novel spatial-frequency cued GFANC (SF-GFANC) method that exploits both three-dimensional (3D) spatial and frequency information of the noise source. Specifically, a multi-task convolutional recurrent neural network (CRNN) is designed to estimate the source distance, elevation angle, and azimuth angle as spatial cues, while predicting the combination weights of sub control filters as frequency cues. These spatial-frequency cues jointly guide the generation of the appropriate control filter. In addition, a theoretical analysis of the optimal control filter in reverberant environments is presented, highlighting the importance of 3D spatially conditioned control filter design. Evaluations using both simulated and measured acoustic paths demonstrate that the CRNN is robust to unseen acoustic environments and noise types. Furthermore, the results confirm that SF-GFANC outperforms representative ANC algorithms when handling noise sources across diverse 3D locations and frequency characteristics in reverberant environments.
