Abstract
This paper presents a novel adaptive structure for audio noise removal, aiming to enhance the performance of noise reduction. The proposed structure consists of a bank of parallel least-mean-squares, time-domain adaptive filters. Multiple microphones are employed to capture the noise source signal, while another microphone records the corrupted speech signal. By passing the recorded noise signals through the parallel adaptive filter bank structure and subtracting the results from the speech signal, the noise is effectively suppressed. Additionally, the noise removal performance is further improved by linearly combining the error signals, which include the noise-free speech signal. The effectiveness of the proposed adaptive structure is demonstrated through theoretical analysis and numerical simulations, highlighting its superior noise removal performance compared to traditional acoustic noise cancellation approaches.
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The speech signal of airplane captain that is depicted in Fig. 6 can be downloaded from the following website: https://www.audiomicro.com/sound-effects/voice-prompts-and-spoken-phrases/pilots
References
Widrow, B., et al.: Adaptive noise cancelling: principles and applications. Proc. IEEE 63(12), 1692–1716 (1975)
Greenberg, J.E.: Modified LMS algorithms for speech processing with an adaptive noise canceller. IEEE Trans. Speech Audio Process. 6(4), 338–351 (1998)
Zhang, S., Zhang, J., Han, H.: Robust variable step-size decorrelation normalized least-mean-square algorithm and its application to acoustic echo cancellation. IEEE/ACM Trans. Audio Speech Lang. Process. 24(12), 2368–2376 (2016)
Gorriz, J.M., et al.: A novel LMS algorithm applied to adaptive noise cancellation. IEEE Signal Process. Lett. 16(1), 34–37 (2009)
Bjarnason, E.: Analysis of the filtered-x LMS algorithm. IEEE Trans. Speech Audio Process. 3(6), 504–514 (1995)
Tan, L., Jiang, J.: Adaptive Volterra filters for active control of nonlinear noise processes. IEEE Trans. Signal Process. 49(8), 1667–1676 (2001)
Das, D.P., Panda, G.: Active mitigation of nonlinear noise processes using a novel filtered-s LMS algorithm. IEEE Trans. Speech Audio Process. 12(3), 313–322 (2004)
Barik, A., et. al.: LMS adaptive multiple sub-filters based acoustic echo cancellation. In: International Conference on Computer and Communication Technology (ICCCT), 17–19 Sept. Allahabad, India (2010)
Emura, Satoru: Residual echo reduction for multichannel acoustic echo cancelers with a complex-valued residual echo estimate. IEEE/ACM Trans. Audio Speech Lang. Process. 26(3), 485–500 (2018)
Malik, S., et al.: Double-talk robust multichannel acoustic echo cancellation using least-squares MIMO adaptive filtering: transversal, array, and lattice forms. IEEE Trans. Signal Process. 68(1), 4887–4902 (2020)
Johansson, S., et al.: Evaluation of multiple reference active noise control algorithms on Dornier 328 aircraft data. IEEE Trans. Speech Audio Process. 7(4), 473–477 (1999)
Sicuranza, G.L., Carini, A.: Filtered-x affine projection algorithm for multichannel active noise control using second order volterra filters. IEEE Signal Process. Lett. 11(11), 853–857 (2004)
Malik, S., Enzner, G.: Recursive Bayesian control of multichannel acoustic echo cancellation. IEEE Signal Process. Lett. 18(11), 619–622 (2011)
Valero, M.L., Habets, E.A.P.: Low-complexity multi-microphone acoustic echo control in the short-time Fourier transform domain. IEEE/ACM Trans. Audio Speech Lang. Process. 27(3), 595–609 (2019)
Sayed, A.H.: Adaptive Filters. Wiley, New Jersey (2008)
Hajiabadi, M., Hodtani, G.A., Khoshbin, H.: Robust learning over multitask adaptive networks with wireless communication links. IEEE Trans. Circuits Syst. II Express Briefs 66(6), 1083–1087 (2019)
Laub, A.J.: Matrix Analysis for Scientists and Engineers, p. 184. SIAM Society for Industrial and Applied Mathematics, Philadelphia (2005)
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All tasks related to this article, including writing the paper, preparing figures, computer simulations, and theoretical analyses, have been carried out by the responsible author of the article. Mojtaba Hajiabadi wrote the main manuscript text and prepared all figures and computer simulations.
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The interest of author lies in adaptive filters, and this research has been conducted to study this field. The author declares that he has no competing interests as defined by Springer, or other interests that might be perceived to influence the results and/or discussion reported in this paper.
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Hajiabadi, M. Exploring the potential of parallel adaptive filters for audio noise removal. SIViP 18, 445–454 (2024). https://doi.org/10.1007/s11760-023-02771-0
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DOI: https://doi.org/10.1007/s11760-023-02771-0