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Active broadband sound quality control algorithm with accurate predefined sound pressure level

机译:具有精确的预定义声压级的有源宽带音质控制算法

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The aim of conventional active noise control (ANC) system is to attenuate the sound pressure level (SPL) of unwanted noise as much as possible. However, it is desirable to retain some of the sound with a specified spectrum to meet the requirements of human perception in various applications. This is particularly true in automotive vehicle applications in which some engine noise is to be retained. An active noise equalizer (ANE) is proposed to meet this requirement. This paper proposes an active broadband sound quality control algorithm, based on traditional broadband ANE. The algorithm can not only shape the spectrum of the residual noise according to the sound quality filter, but also control the SPL of the residual noise to satisfy a predefined target accurately, when the amplitude of the disturbance noise changes. This aim is accomplished by introducing a gain factor which is adaptively determined based on the power of the instantaneous disturbance noise and the predefined target. This algorithm has four control modes according to the value of the gain factor. Simulations are carried out to validate the performance of the proposed algorithm using both simulated noise and real vehicle cabin noise. Simulation results show that the SPL error of the proposed control algorithm is within 2 dB of the predetermined target in the majority of cases simulated. The tracking capability of the proposed algorithm is shown to exhibit good performance in the presence of a time varying SPL target and time varying disturbance noise. (C) 2016 Elsevier Ltd. All rights reserved.
机译:常规有源噪声控制(ANC)系统的目的是尽可能地降低有害噪声的声压级(SPL)。然而,期望保留具有指定频谱的一些声音以满足各种应用中的人类感知的需求。在要保留一些发动机噪声的汽车应用中尤其如此。提出了一种有源噪声均衡器(ANE)来满足这一要求。提出了一种基于传统宽带ANE的主动宽带音质控制算法。该算法不仅可以根据音质滤波器对残留噪声的频谱进行整形,而且还可以在干扰噪声的幅度发生变化时,控制残留噪声的SPL精确地满足预定目标。该目的是通过引入基于瞬时干扰噪声和预定目标的功率自适应确定的增益因子来实现的。该算法根据增益因子的值具有四种控制模式。进行了仿真,以使用仿真噪声和实际车厢噪声来验证所提出算法的性能。仿真结果表明,在大多数情况下,所提出的控制算法的SPL误差均在预定目标的2 dB之内。在存在时变SPL目标和时变干扰噪声的情况下,所提出算法的跟踪能力显示出良好的性能。 (C)2016 Elsevier Ltd.保留所有权利。

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