請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80430完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 曹恆偉(Hen-Wai Tsao) | |
| dc.contributor.author | Chih-Hsiang Yu | en |
| dc.contributor.author | 游智翔 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:06:31Z | - |
| dc.date.available | 2022-01-17 | |
| dc.date.available | 2022-11-24T03:06:31Z | - |
| dc.date.copyright | 2022-01-17 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-12-24 | |
| dc.identifier.citation | [1] 新北市政府環境保護局, “噪聲管制介紹.” [Online]. Available: https://www.epd.ntpc.gov.tw/Article?catID=309 (Accessd on: 2021/07/31). [2] M. Sadeghian and M. Bandpy, “Technologies for aircraft noise reduction: Re-view paper,” J Aeronaut Aerospace Eng, vol. 9, p. 218, 2020. [3] Y. Kajikawa, W.-S. Gan, and S. M. Kuo, “Recent advances on active noise control: open issues and innovative applications,” APSIPA Transactions on Signal and Information Processing, vol. 1, 2012. [4] K. Iwai, S. Kinoshita, and Y. Kajikawa, “Multichannel feedforward active noise control system combined with noise source separation by microphone arrays,” Journal of Sound and Vibration, vol. 453, pp. 151–173, 2019. [5] C.-Y. Chang, A. Siswanto, C.-Y. Ho, T.-K. Yeh, Y.-R. Chen, and S. M. Kuo,“Listening in a noisy environment: Integration of active noise control in audio products,” IEEE Consumer Electronics Magazine, vol. 5, no. 4, pp. 34–43, 2016. [6] M. T. Akhtar and W. Mitsuhashi, “Improving performance of fxlms algorithm for active noise control of impulsive noise,” Journal of Sound and Vibration, vol. 327, no. 3-5, pp. 647–656, 2009. [7] G. Sun, M. Li, and T. C. Lim, “Enhanced filtered-x least mean m-estimate algorithm for active impulsive noise control,” Applied Acoustics, vol. 90, pp.31–41, 2015. [8] Y. Zhou, Q. Zhang, and Y. Yin, “Active control of impulsive noise with symmetric α-stable distribution based on an improved step-size normalized adaptive algorithm,” Mechanical Systems and Signal Processing, vol. 56, pp. 320–339,2015. [9] N. V. George and G. Panda, “A robust filtered-s lms algorithm for nonlinear active noise control,” Applied Acoustics, vol. 73, no. 8, pp. 836–841, 2012. [10] W. Liu, P. P. Pokharel, and J. C. Principe, “Correntropy: Properties and applications in non-gaussian signal processing,” IEEE Transactions on signal processing, vol. 55, no. 11, pp. 5286–5298, 2007. [11] S. S. Haykin, Adaptive filter theory. Pearson Education India, 2008. [12] G. C. Tocci, “Handbook of acoustical measurements and noise control,” 1993. [13] I. L. Vér and L. L. Beranek, Noise and vibration control engineering: principles and applications. John Wiley Sons, 2005. [14] P. Lueg, “Process of silencing sound oscillations,” US pat ent 2043416, 1936. [15] H. F. Olson and E. G. May, “Electronic sound absorber,” The Journal of the Acoustical Society of America, vol. 25, no. 6, pp. 1130–1136, 1953. [16] J. C. Burgess, “Active adaptive sound control in a duct: A computer simulation,” The Journal of the Acoustical Society of America, vol. 70, no. 3, pp.715–726, 1981. [17] S. M. Kuo, Y.-R. Chen, C.-Y. Chang, and C.-W. Lai, “Development and evaluation of light-weight active noise cancellation earphones,” Applied Sciences, vol. 8, no. 7, p.1178, 2018. [18] A. Siswanto, C.-Y. Chang, and S. M. Kuo, “Active noise control for headrests,” in 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). IEEE, 2015, pp. 688–692. [19] W. Jung, S. J. Elliott, and J. Cheer, “Local active control of road noise inside a vehicle,” Mechanical Systems and Signal Processing, vol. 121, pp. 144–157, 2019. [20] C.-Y. Ho, K.-K. Shyu, C.-Y. Chang, and S. M. Kuo, “Integrated active noise control for open-fit hearing aids with customized filter,” Applied Acoustics, vol. 137, pp. 1–8, 2018. [21] H. Lokesha, S. Veena, A. Safiya, M. A. Kumar, and K. Basavaraju, “Evaluation of delfino (tms320f28379d) processor for helmet anc application,” in 2019 Global Conference for Advancement in Technology (GCAT). IEEE, 2019, pp. 1–4. [22] I. D. Landau, R. Meléndez, L. Dugard, and G. Buche, “Robust and adaptive feedback noise attenuation in ducts,” IEEE Transactions on Control Systems Technology, vol. 27, no. 2, pp. 872–879, 2017. [23] S. M. Kuo and D. R. Morgan, Active noise control systems. Wiley, New York, 1996, vol. 4. [24] ——, “Active noise control: a tutorial review,” Proceedings of the IEEE, vol. 87, no. 6, pp. 943–973, 1999. [25] D. Morgan, “An analysis of multiple correlation cancellation loops with a filter in the auxiliary path,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 28, no. 4, pp. 454–467, 1980. [26] J. Cheer and S. J. Elliott, “Active noise control of a diesel generator in a luxury yacht,” Applied Acoustics, vol. 105, pp. 209–214, 2016. [27] L. Lu and H. Zhao, “Improved filtered-x least mean kurtosis algorithm for active noise control,” Circuits, Systems, and Signal Processing, vol. 36, no. 4, pp. 1586–1603, 2017. [28] Y. Wang, H. Guo, Y. Li, N. Liu, and C. Yang, “Active control for vehicle interior noise based on dwt-fxlms algorithm using a piezoelectric feedback system,” Applied Acoustics, vol. 167, p. 107409, 2020. [29] M. Bouchard and S. Quednau, “Multichannel recursive-least-square algorithms and fast-transversal-filter algorithms for active noise control and sound reproduction systems,” IEEE transactions on speech and audio processing, vol. 8, no. 5, pp. 606–618, 2000. [30] R. M. Reddy, I. M. Panahi, and R. Briggs, “Hybrid fxrls-fxnlms adaptive algorithm for active noise control in fmri application,” IEEE Transactions on Control Systems Technology, vol. 19, no. 2, pp. 474–480, 2010. [31] M. Bouchard, “Multichannel affine and fast affine projection algorithms for active noise control and acoustic equalization systems,” IEEE Transactions on Speech and Audio Processing, vol. 11, no. 1, pp. 54–60, 2003. [32] F. Albu, “Efficient multichannel filtered-x affine projection algorithm for active noise control,” Electronics Letters, vol. 42, no. 7, pp. 421–423, 2006. [33] M. Ferrer, A. Gonzalez, M. de Diego, and G. Pinero, “Fast affine projection algorithms for filtered-x multichannel active noise control,” IEEE transactions on audio, speech, and language processing, vol. 16, no. 8, pp. 1396–1408, 2008. [34] T. Padhi, M. Chandra, A. Kar, and M. Swamy, “Design and analysis of an improved hybrid active noise control system,” Applied Acoustics, vol. 127, pp. 260–269, 2017. [35] T. Padhi, M. Chandra, and A. Kar, “Performance evaluation of hybrid active noise control system with online secondary path modeling,” Applied Acoustics, vol. 133, pp. 215–226, 2018. [36] L. Lu, K.-L. Yin, R. C. de Lamare, Z. Zheng, Y. Yu, X. Yang, and B. Chen,“A survey on active noise control in the past decade–part i: Linear systems,” Signal Processing, p. 108039, 2021. [37] L. J. Eriksson and M. C. Allie, “Use of random noise for on-line transducer modeling in an adaptive active attenuation system,” The Journal of the Acoustical Society of America, vol. 85, no. 2, pp. 797–802, 1989. [38] M. T. Akhtar, M. Abe, and M. Kawamata, “A new variable step size lms algorithm-based method for improved online secondary path modeling in active noise control systems,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 14, no. 2, pp. 720–726, 2006. [39] S. Ahmed, M. T. Akhtar, and X. Zhang, “Robust auxiliary-noise-power scheduling in active noise control systems with online secondary path modeling,” IEEE transactions on audio, speech, and language processing, vol. 21, no. 4, pp. 749–761, 2012. [40] S. C. Douglas, “Fast implementations of the filtered-x lms and lms algorithms for multichannel active noise control,” IEEE Transactions on speech and audio processing, vol. 7, no. 4, pp. 454–465, 1999. [41] W. S. Gan and S. M. Kuo, “An integrated audio and active noise control headset,” IEEE Transactions on Consumer Electronics, vol. 48, no. 2, pp. 242–247, 2002. [42] 國家教育研究院雙語詞彙、學術名詞暨辭書資訊網, “Impulsive noise - 脈衝性噪音,”2002. [Online]. Available: https://terms.naer.edu.tw/detail/1318529/ (Accessd on: 2021/06/25). [43] A. Zeb, A. Mirza, Q. U. Khan, and S. A. Sheikh, “Improving performance of fxrls algorithm for active noise control of impulsive noise,” Applied Acoustics, vol. 116, pp. 364–374, 2017. [44] M. Zimmermann and K. Dostert, “Analysis and modeling of impulsive noise in broad-band powerline communications,” IEEE transactions on Electromagnetic compatibility, vol. 44, no. 1, pp. 249–258, 2002. [45] Y. L. Zhou, Y. X. Yin, and Q. Z. Zhang, “An optimal repetitive control algorithm for periodic impulsive noise attenuation in a non-minimum phase anc system,” Applied acoustics, vol. 74, no. 10, pp. 1175–1181, 2013. [46] L. Wu and X. Qiu, “An m-estimator based algorithm for active impulse-like noise control,” Applied acoustics, vol. 74, no. 3, pp. 407–412, 2013. [47] M. Shao and C. L. Nikias, “Signal processing with fractional lower order moments: stable processes and their applications,” Proceedings of the IEEE, vol. 81, no. 7, pp. 986–1010, 1993. [48] P. Kidmose, “Alpha-stable distributions in signal processing of audio signals,” in 41st Conference on Simulation and Modelling, 2000, pp. 87–94. [49] K. L. Chung and K. Zhong, A course in probability theory. Academic press, 2001. [50] R. Leahy, Z. Zhou, and Y.-C. Hsu, “Adaptive filtering of stable processes for active attenuation of impulsive noise,” in 1995 international conference on acoustics, speech, and signal processing, vol. 5. IEEE, 1995, pp. 2983–2986. [51] M. T. Akhtar and W. Mitsuhashi, “Improving robustness of filtered-x least mean p-power algorithm for active attenuation of standard symmetric-α-stable impulsive noise,” Applied Acoustics, vol. 72, no. 9, pp. 688–694, 2011. [52] X. Sun, S. M. Kuo, and G. Meng, “Adaptive algorithm for active control of impulsive noise,” Journal of sound and vibration, vol. 291, no. 1-2, pp. 516–522, 2006. [53] P. Thanigai, S. M. Kuo, and R. Yenduri, “Nonlinear active noise control for infant incubators in neo-natal intensive care units,” in 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP’07, vol. 1. IEEE, 2007, pp. I–109. [54] G. Sun, M. Li, and T. C. Lim, “A family of threshold based robust adaptive algorithms for active impulsive noise control,” Applied Acoustics, vol. 97, pp. 30–36, 2015. [55] L. Wu, H. He, and X. Qiu, “An active impulsive noise control algorithm with logarithmic transformation,” IEEE transactions on audio, speech, and language processing, vol. 19, no. 4, pp. 1041–1044, 2010. [56] L. Lu and H. Zhao, “Active impulsive noise control using maximum correntropy with adaptive kernel size,” Mechanical Systems and Signal Processing, vol. 87, pp. 180–191, 2017. [57] M. T. Akhtar, “Binormalized data-reusing adaptive filtering algorithm for active control of impulsive sources,” Digital Signal Processing, vol. 49, pp. 56–64, 2016. [58] N. C. Kurian, K. Patel, and N. V. George, “Robust active noise control: An information theoretic learning approach,” Applied Acoustics, vol. 117, pp. 180–184, 2017. [59] Y. Zhu, H. Zhao, X. Zeng, and B. Chen, “Robust generalized maximum correntropy criterion algorithms for active noise control,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 1282–1292, 2020. [60] F. Gu, S. Chen, Z. Zhou, and Y. Jiang, “An enhanced normalized step-size algorithm based on adjustable nonlinear transformation function for active control of impulsive noise,” Applied Acoustics, vol. 176, p. 107853, 2021. [61] Y. Zou, S.-C. Chan, and T.-S. Ng, “Least mean m-estimate algorithms for robust adaptive filtering in impulse noise,” IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, vol. 47, no. 12, pp. 1564–1569, 2000. [62] S.-C. Chan and Y.-X. Zou, “A recursive least m-estimate algorithm for robust adaptive filtering in impulsive noise: fast algorithm and convergence performance analysis,” IEEE Transactions on Signal Processing, vol. 52, no. 4, pp. 975–991, 2004. [63] J. G. Gonzalez, J. L. Paredes, and G. R. Arce, “Zero-order statistics: A mathematical framework for the processing and characterization of very impulsive signals,” IEEE Transactions on Signal Processing, vol. 54, no. 10, pp. 3839–3851, 2006. [64] M. Bergamasco, F. Della Rossa, and L. Piroddi, “Active noise control with on-line estimation of non-gaussian noise characteristics,” Journal of Sound and Vibration, vol. 331, no. 1, pp. 27–40, 2012. [65] D. Erdogmus, Information theoretic learning: Renyi’s entropy and its applications to adaptive system training. University of Florida, 2002. [66] A. Singh and J. C. Principe, “Using correntropy as a cost function in linear adaptive filters,” in 2009 International Joint Conference on Neural Networks. IEEE, 2009, pp. 2950–2955. [67] A. Carini and G. L. Sicuranza, “Transient and steady-state analysis of filtered-x affine projection algorithms,” IEEE transactions on signal processing, vol. 54, no. 2, pp. 665–678, 2006. [68] J. Guo, F. Yang, and J. Yang, “Convergence analysis of the conventional filtered-x affine projection algorithm for active noise control,” Signal Processing, vol. 170, p. 107437, 2020. [69] M. Ferrer, A. Gonzalez, M. de Diego, and G. Pinero, “Transient analysis of the conventional filtered-x affine projection algorithm for active noise control,” IEEE transactions on audio, speech, and language processing, vol. 19, no. 3, pp. 652–657, 2010. [70] M. Ferrer, M. de Diego, A. Gonzalez, and G. Piñero, “Steady-state mean square performance of the multichannel filtered-x affine projection algorithm,” IEEE transactions on signal processing, vol. 60, no. 6, pp. 2771–2785, 2012. [71] J. Guo, F. Yang, and J. Yang, “Mean-square performance of the modified filtered-x affine projection algorithm,” Circuits, Systems, and Signal Processing, pp. 1–15, 2020. [72] M. Z. A. Bhotto and A. Antoniou, “Affine-projection-like adaptive-filtering algorithms using gradient-based step size,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 61, no. 7, pp. 2048–2056, 2014. [73] H.-C. Shin and A. H. Sayed, “Mean-square performance of a family of affine projection algorithms,” IEEE Transactions on Signal Processing, vol. 52, no. 1, pp. 90–102, 2004. [74] P. Song and H. Zhao, “Affine-projection-like m-estimate adaptive filter for robust filtering in impulse noise,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 66, no. 12, pp. 2087–2091, 2019. [75] M. F. Møller, “A scaled conjugate gradient algorithm for fast supervised learning,” Neural networks, vol. 6, no. 4, pp. 525–533, 1993. [76] R. Weron, “On the chambers-mallows-stuck method for simulating skewed stable random variables,” Statistics probability letters, vol. 28, no. 2, pp. 165– 171, 1996. [77] O. Solomon Jr, “Psd computations using welch’s method,” NASA STI/Recon Technical Report N, vol. 92, p. 23584, 1991. [78] P. Li and X. Yu, “Active noise cancellation algorithms for impulsive noise,” Mechanical systems and signal processing, vol. 36, no. 2, pp. 630–635, 2013. [79] “Jaguar vehicle engine noise.” [Online]. Available: https://www.jaguar.com/ jaguar-range/f-type/index.html (Accessd on: 2021/08/31). | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80430 | - |
| dc.description.abstract | 脈衝性噪音無所不在，此類噪音在短時間內會達到極高的分貝值，更可能造成人類聽力受損。快速且有效地降低環境脈衝噪音是主動式降噪技術的重要訴求之一。然而，大部分的主動式噪音控制演算法在面對脈衝性時，其噪音消除能力與收斂速度皆有不足。本研究基於最大相關熵準則以及仿射投影演算法，提出了一個兼具收斂速度與降噪能力的MFxAPLMCC (Modified Filtered-x Affine-Projection-Like Maximum Correntropy Criterion)演算法。我們提出一個新的目標函數：最大化多次資料再利用之期望訊號與次級路徑輸出訊號的相關熵和，並且推導了最佳步長使得演算法可以有更好的收斂效能，最後考量運算複雜度的情況下，利用線性近似設計出最佳步長，同時也證明了演算法的穩定性。本研究採用數值模擬的方式驗證演算法之有效性，評量指標主要是平均噪音消除率。輸入信號考慮了:(1)白高斯噪音、(2)粉色噪音、(3)脈衝噪音(對稱阿爾發穩定分布)、(4)混合正弦噪音與脈衝噪音以及(5)實際車內引擎噪音等五種類型的信號。此外，我們也模擬了主要路徑改變時，所提出的MFxAPLMCC演算法之追蹤性能表現。模擬結果顯示MFxAPLMCC演算法相較於其他六種常見的噪音消除演算法，在收斂速度、降噪能力以及追蹤速度上均有顯著的增進。最後我們也驗證了演算法穩定度的理論分析結果與數值模擬結果的一致性。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:06:31Z (GMT). No. of bitstreams: 1 U0001-2312202122103700.pdf: 14109628 bytes, checksum: a1056400df3742d5141d3f9a71a97f9d (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 目錄 致謝 i 摘要 ii Abstract iii 目錄 v 圖目錄 ix 表目錄 xi 第一章 緒論 1 1.1 前言 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 研究動機 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 論文架構 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 第二章 主動噪音控制（ANC）介紹 4 2.1 噪音消除技術 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1.1 被動噪音控制 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1.2 主動噪音控制 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.3 ANC 架構概述 . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 前饋式 ANC 架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2.1 系統架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2.2 FxLMS 演算法 . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2.3 其他演算法文獻回顧 . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3 反饋式 ANC 架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.4 混合式 ANC 架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.5 次級路徑模型估計 . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.6 其他類型 ANC 系統 . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.6.1 多通道 ANC 系統 . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.6.2 音頻集成 ANC 系統 . . . . . . . . . . . . . . . . . . . . . . . . 20 第三章 主動式脈衝噪音控制（AINC） 22 3.1 ANC 脈衝噪音問題 . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.2 脈衝噪音數學模型 . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.3 AINC 既有演算法回顧 . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.3.1 FxLMP 演算法 . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.3.2 Sun 與 Akhtar 的演算法 . . . . . . . . . . . . . . . . . . . . . . 29 3.3.3 FxLMM 演算法 . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.3.4 FxlogLMS 演算法 . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.3.5 RFxLMS 演算法 . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.3.6 FxMCC 演算法 . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 第四章 基於最大相關熵準則與仿射投影的主動脈衝噪音控制演算法 39 4.1 最大相關熵準則（MCC） . . . . . . . . . . . . . . . . . . . . . . . 39 4.2 仿射投影（AP）算法應用於 ANC 系統 . . . . . . . . . . . . . . . 41 4.2.1 CFxAP 演算法 . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.2.2 MFxAP 演算法 . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.3 MFxAPLMCC 演算法 . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.3.1 係數更新式推導 . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.3.2 最佳步長推導 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.4 演算法效能分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.4.1 穩定性分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.4.2 運算複雜度分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.5 C-MFxAPLMCC 演算法 . . . . . . . . . . . . . . . . . . . . . . . . 59 第五章 系統模擬結果分析 61 5.1 實驗模擬環境介紹 . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 5.2 演算法效能比較 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.2.1 實驗一：白高斯噪音（white Gaussian noise） . . . . . . . . . . 65 5.2.2 實驗二：粉色噪音（pink noise） . . . . . . . . . . . . . . . . . 66 5.2.3 實驗三：脈衝噪音（impulsive noise） . . . . . . . . . . . . . . 67 5.2.4 實驗四：複合正弦噪音（sinusoidal noise）混和脈衝噪音 . . . . 73 5.2.5 實驗五：實際音訊（audio）之車內引擎噪音 . . . . . . . . . . . 75 5.3 演算法效能分析驗證 . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5.3.1 追蹤能力評估 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5.3.2 穩定性分析模擬驗證 . . . . . . . . . . . . . . . . . . . . . . . . 78 5.4 演算法參數選擇討論 . . . . . . . . . . . . . . . . . . . . . . . . . . 80 5.4.1 步長參數（step-size） . . . . . . . . . . . . . . . . . . . . . . . 80 5.4.2 核大小（kernel size）. . . . . . . . . . . . . . . . . . . . . . . . 81 5.4.3 投影階數（projection order） . . . . . . . . . . . . . . . . . . . 82 5.5 C-MFxAPLMCC 演算法的模擬結果分析 . . . . . . . . . . . . . . . 84 第六章 結論與未來展望 86 6.1 結論 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 6.2 未來展望 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 參考文獻 89 | |
| dc.language.iso | zh-TW | |
| dc.subject | 仿射投影 | zh_TW |
| dc.subject | 最大相關熵準則 | zh_TW |
| dc.subject | 對稱阿爾發穩定脈衝雜訊 | zh_TW |
| dc.subject | 主動噪音消除 | zh_TW |
| dc.subject | maximum correntropy criterion (MCC) | en |
| dc.subject | Active noise cancellation | en |
| dc.subject | symmetric Alpha stable impulse noise | en |
| dc.subject | affine projection | en |
| dc.title | 基於類仿射投影演算法的前饋式主動噪音消除之設計 | zh_TW |
| dc.title | Design of a Feedforward Active Noise Control based on Affine-Projection-Like Algorithms | en |
| dc.date.schoolyear | 110-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 錢膺仁(Ying-Ren Chien) | |
| dc.contributor.oralexamcommittee | 張大中(Hsin-Tsai Liu),張政元(Chih-Yang Tseng) | |
| dc.subject.keyword | 主動噪音消除,仿射投影,最大相關熵準則,對稱阿爾發穩定脈衝雜訊, | zh_TW |
| dc.subject.keyword | Active noise cancellation,affine projection,maximum correntropy criterion (MCC),symmetric Alpha stable impulse noise, | en |
| dc.relation.page | 99 | |
| dc.identifier.doi | 10.6342/NTU202104567 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-12-27 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-2312202122103700.pdf 授權僅限NTU校內IP使用（校園外請利用VPN校外連線服務） | 13.78 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。