论文标题
基于不同卷积神经网络的选择性固定过滤器主动噪声控制的性能评估
Performance Evaluation of Selective Fixed-filter Active Noise Control based on Different Convolutional Neural Networks
论文作者
论文摘要
由于其快速响应时间和高度的鲁棒性，选择性固定过滤器主动噪声控制（SFANC）方法似乎是在各种实用的活动噪声控制（ANC）系统中广泛使用的可行候选者。与常规的固定过滤ANC方法相比，SFANC可以为不同类型的噪声选择预训练的控制过滤器。因此，深度学习技术可以用于SFANC方法中，以使最适当的控制过滤器更灵活地选择衰减各种噪声。此外，在深层神经网络的帮助下，可以自动从噪声数据而不是通过试用和错误来学习选择策略，从而大大简化和改善了ANC设计的可实用性。因此，本文研究了基于不同的一维和二维卷积神经网络的SFANC的性能。此外，我们对几种网络培训策略进行了比较分析，并发现微调可以提高选择性的性能。
Due to its rapid response time and a high degree of robustness, the selective fixed-filter active noise control (SFANC) method appears to be a viable candidate for widespread use in a variety of practical active noise control (ANC) systems. In comparison to conventional fixed-filter ANC methods, SFANC can select the pre-trained control filters for different types of noise. Deep learning technologies, thus, can be used in SFANC methods to enable a more flexible selection of the most appropriate control filters for attenuating various noises. Furthermore, with the assistance of a deep neural network, the selecting strategy can be learned automatically from noise data rather than through trial and error, which significantly simplifies and improves the practicability of ANC design. Therefore, this paper investigates the performance of SFANC based on different one-dimensional and two-dimensional convolutional neural networks. Additionally, we conducted comparative analyses of several network training strategies and discovered that fine-tuning could improve selection performance.
