Abstract Active sound quality control (ASQC) of vehicle interior noise is to reshape the sound spectrum by considering human perception. But current ASQC method mainly controls the interior noise amplitude, ignoring the time–frequency characteristics, and active control of sharpness is a problem. According to psychoacoustic indices, the original noise needs to be decomposed into subbands to obtain targeted control. Thus, this study presents a hybrid time–frequency domain active noise equalization (TFD-ANE) algorithm based on stationary discrete wavelet transform. For get a supporting effect, a variable step-size FxLMS (VS-FxLMS) and a normalized frequency-domain block FxLMS are inspected and determined for each subband noise suppression. It can be found NFB-FxLMS can control higher frequency noise well, while VS-FxLMS just shows better control effect on the lowest frequency subband. To verify the effectiveness of proposed method, ASQC simulations are performed by using the basic ANE, VS-ANE, NFB-ANE and TFD-ANE algorithms. Moreover, three psychoacoustic indices, loudness, sharpness, and roughness, with the utmost impact on the interior sound quality, are considered to quantify the noise improvement effect. Results show that TFD-ANE achieves the optimum performance, where the loudness and roughness are further reduced by more than 38% and 46% compared with other three algorithms. In terms of sharpness, the ANE and VS-ANE are ineffective, and the NFB-FxLMS cannot improve the sharpness of nonstationary interior noise. However, the proposed TFD-ANE can reduce the sharpness by approximately 40%, which suggests a promising approach to the ASQC of vehicle interior noises and other sound-related fields in engineering.

中文翻译：

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基于平稳离散小波变换的车内噪声主动声质量混合时频算法

摘要 车内噪声主动声质量控制(ASQC)是通过考虑人的感知来重塑声谱。但是目前的ASQC方法主要是控制内部噪声幅度，忽略了时频特性，锐度的主动控制是一个问题。根据心理声学指标，需要将原始噪声分解为子带以获得有针对性的控制。因此，本研究提出了一种基于平稳离散小波变换的混合时频域有源噪声均衡 (TFD-ANE) 算法。为了获得支持效果，针对每个子带噪声抑制检查并确定可变步长FxLMS（VS-FxLMS）和归一化频域块FxLMS。可以发现NFB-FxLMS可以很好地控制高频噪声，而VS-FxLMS只是在最低频率子带上表现出更好的控制效果。为了验证所提出方法的有效性，使用基本的 ANE、VS-ANE、NFB-ANE 和 TFD-ANE 算法进行了 ASQC 仿真。此外，考虑到对室内音质影响最大的三个心理声学指标，响度、锐度和粗糙度，来量化噪声改善效果。结果表明，TFD-ANE 实现了最佳性能，与其他三种算法相比，响度和粗糙度进一步降低了 38% 和 46% 以上。在锐度方面，ANE和VS-ANE效果不佳，NFB-FxLMS无法提高非平稳内部噪声的锐度。然而，提出的 TFD-ANE 可以将锐度降低约 40%，