Active control of vehicle interior noise via attention mechanism and prioritized experience replay

Although Deep Reinforcement Learning (DRL) has shown potential in Active Noise Control (ANC), its effectiveness is often limited by the random and non-stationary sound field inside the vehicle. The current methods generally have insufficient feature extraction, and important training samples cannot be utilized effectively. In order to solve the above shortcomings, this paper proposes an enhanced generative filter ANC method, which combines the Channel-Spatial Attention Mechanism with Prioritized Experience Replay (GFANC-RL-CP). Specifically, the system integrated with Channel-Spatial Attention Mechanism (CBAM) is used to achieve the extraction of key channel and temporal features, so as to improve the system’s ability to identify complex noise features. In addition, by deploying the Prioritized Experience Replay (PER) mechanism to improve the learning process, high-value samples can be purposefully reused. Through the verification of real vehicle data, it is found that the performance was clearly improved. The proposed method reduced the training time by about 50%. Compared with traditional FxLMS and GFANC-RL, it achieves improvements of 8–16 dB and 4–8 dB respectively, and has a good noise suppression effect in the entire frequency range of 0–8000 Hz.