The selective delayless subband structure stands out as a promising algorithmic choice for the multi-channel active control of vehicle interior noise, particularly in the context of road noise. This type of algorithm reduces the eigenvalue spread of the autocorrelation matrix of the signal by decomposing the signal into subbands, and the desired subbands are activated selectively, thus achieving a significant performance improvement while consuming less computational resources compared with the traditional fullband algorithms. Nevertheless, the effectiveness of the subband algorithm appears to be closely tied to the configuration of the primary disturbance signal. In cases where the primary disturbance signal encompasses both broadband and tonal components, the performance advantage of the subband algorithm becomes constrained. In this paper, we conduct a thorough comparative analysis of two extensively employed subband algorithms, namely the Morgan and the Milani methods, through simulations employing data obtained from a multi-channel active noise control (ANC) headrest system. The results indicate that the Morgan method outperforms the Milani method when configured optimally. Subsequently, we propose an enhanced version of the subband algorithm based on the Morgan method. The enhanced algorithm incorporates an additional sinusoidal noise canceller (SNC) subsystem and a narrowband active noise control (NANC) subsystem based on local secondary path modeling to address tonal components, while the selective subband structure is employed for controlling broadband components. In addition, the computational complexity of the algorithm is analyzed. The effectiveness of the proposed algorithm is validated through numerous simulations and the ANC tests conducted on an actual vehicle utilizing the multi-channel ANC headrest system. The performance of the proposed algorithm surpasses that of traditional selective subband and fullband algorithms, and balanced binaural noise reduction is achieved.

中文翻译：

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车辆多通道有源噪声控制系统的独立于主扰动配置的增强型选择性无延迟子带算法


选择性无延迟子带结构是车辆内部噪声多通道主动控制的一种有前途的算法选择，特别是在道路噪声的背景下。此类算法通过将信号分解为子带来减少信号自相关矩阵的特征值扩展，并选择性地激活所需的子带，从而与传统全带算法相比，在消耗更少的计算资源的同时实现了显着的性能提升。然而，子带算法的有效性似乎与主要干扰信号的配置密切相关。在主要干扰信号包含宽带和音调分量的情况下，子带算法的性能优势受到限制。在本文中，我们通过使用从多通道主动噪声控制（ANC）头枕系统获得的数据进行模拟，对两种广泛使用的子带算法（即摩根法和米拉尼法）进行了彻底的比较分析。结果表明，在优化配置时，Morgan 方法优于 Milani 方法。随后，我们提出了基于摩根方法的子带算法的增强版本。增强算法结合了一个额外的正弦噪声消除器（SNC）子系统和一个基于局部二级路径建模的窄带有源噪声控制（NANC）子系统来处理音调分量，同时采用选择性子带结构来控制宽带分量。此外，还分析了算法的计算复杂度。 通过大量模拟和在使用多通道 ANC 头枕系统的实际车辆上进行的 ANC 测试，验证了所提出算法的有效性。该算法的性能超越了传统的选择性子带和全带算法，实现了平衡的双耳降噪。