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Block coordinate descent based algorithm for computational complexity reduction in multichannel active noise control system
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DOI：10.1016/j.ymssp.2020.107346.png)
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Multichannel active noise control (MCANC) is widely regarded as an effective solution to achieve a significantly large noise-cancellation area in a complicated acoustic field. However, the computational complexity of MCANC algorithms, such as the multichannel filter-x least mean square (McFxLMS) algorithm, grows exponentially with an increased channel count. Many modified algorithms have been proposed to alleviate the complexity but at the expense of noise reduction performance. Till now, the trade-off between computational complexity and noise reduction performance has limited the practical implementation of MCANC. The block coordinate descent McFxLMS (BCD McFxLMS) algorithm proposed in this paper substantially reduces the computation cost of an MCANC system, while maintaining the same noise reduction performance as the conventional McFxLMS algorithm. Furthermore, a momentum mechanism is integrated into the BCD McFxLMS in practice to improve the convergence speed. The simulation and experimental results validate the effectiveness of proposed algorithms when dealing with noise in a realistic environment. (c) 2020 Elsevier Ltd. All rights reserved.
Keyword:
Multichannel active noise control
Block coordinate descent
Multichannel filtered-x least mean square algorithm
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8.9
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1.3W
被引数:
6.6W
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Mean-square performance of the modified frequency-domain block LMS algorithm.png)
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