研究生: |
張顥孆 Chang, Hao-Yin |
---|---|
論文名稱: |
使用麥克風陣列之遠端聲源量測研究 A Measurement Study for Decibel Metering of Remote Objects using Microphone Arrays |
指導教授: |
陳伶志
Chen, Ling-Jyh |
口試委員: |
林子皓
Lin, Tzu-Hao 陳伶志 Chen, Ling-Jyh 賀耀華 Ho, Yao-Hua |
口試日期: | 2022/08/10 |
學位類別: |
碩士 Master |
系所名稱: |
資訊工程學系 Department of Computer Science and Information Engineering |
論文出版年: | 2023 |
畢業學年度: | 111 |
語文別: | 中文 |
論文頁數: | 40 |
中文關鍵詞: | 麥克風陣列 、聲源定位 、噪音照相系統 、分貝 |
英文關鍵詞: | Microphone array, Sound source localization, Noise camera system, Decibel |
研究方法: | 實驗設計法 、 準實驗設計法 |
DOI URL: | http://doi.org/10.6345/NTNU202300766 |
論文種類: | 學術論文 |
相關次數: | 點閱:72 下載:10 |
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交通噪音對人體的健康造成很大的危害,政府在交通噪音的取締上也不遺餘力,民國110年開始,他們採用科技執法推出了一套噪音照相系統,對不當駕駛以及非原廠排氣管進行取締,不但節省了警力,也還給社區一個安寧。然而,該系統的缺點是僅能單向取締,只有在車子經過設定好位置的鏡頭下才有機會進行取締,不但無法知道噪音源的位置,也不能隨意設置攝影機的角度進行全方向取締。
本研究利用麥克風陣列對聲源進行定位且量測聲源實際位置,推估出聲源實際的噪音分貝值來達到全景執法的目的,不論噪音源位置皆可以以實際分貝進行取締,並完成一套低成本的感測系統。
Traffic noise has caused great harm to human health, and the government has spared no effort in banning traffic noise. Since 2021, they implement a noise camera system using technology enforcement, the main purpose is to detect improper driving behavior and non-original exhaust pipes. The system not only saves police effort, but also let communities return to quiet. However, the disadvantages of the system are that it can only detect in single direction, and the drivers can only be banned when they pass through the camera in the system with fixed camera position. Not only is there no way to know where the noise source is, but there's no way to adjust the angle of the camera to ban drivers from all directions.
This research uses the microphone arrays to locate the sound source and measure the actual position of the sound source, and estimate the actual decibel value of the sound source to achieve the purpose of system to ban drivers who drive illegally from all directions. Regardless of the location of the noise source, the actual decibel can be estimated. This research also completed a low-cost sensing system.
行政院環保署(2020), 機動車輛行駛噪音量測方法-影像輔助法
SIEMENS, [Online]. Available: https://community.sw.siemens.com/s/article/sound-fields-free-versus-diffuse-field-near-versus-far-field
Rascon, C., & Meza, I. (2017). Localization of sound sources in robotics: A review. Robotics and Autonomous Systems, 96, 184-210.
Bai, M. R., Lan, S. S., & Huang, J. Y. (2018, July). Time difference of arrival (TDOA)-based acoustic source localization and signal extraction for intelligent audio classification. In 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM) (pp. 632-636). IEEE.
Bot, O. L., Gervaise, C., & Mars, J. I. (2016). Time-difference-of-arrival estimation based on cross recurrence plots, with application to underwater acoustic signals. In Recurrence Plots and Their Quantifications: Expanding Horizons (pp. 265-288). Springer, Cham.
Robles, G., Fresno, J. M., & Martínez-Tarifa, J. M. (2018). Radio-frequency localization of multiple partial discharges sources with two receivers. Sensors, 18(5), 1410.
DiBiase, J. H. (2000). A high-accuracy, low-latency technique for talker localization in reverberant environments using microphone arrays. Brown University.
Grondin, F., & Michaud, F. (2019). Lightweight and optimized sound source localization and tracking methods for open and closed microphone array configurations. Robotics and Autonomous Systems, 113, 63-80.
Grondin, F., Létourneau, D., Godin, C., Lauzon, J. S., Vincent, J., Michaud, S., ... & Michaud, F. (2019). ODAS: Open embedded audition system. Frontiers in Robotics and AI, 125.
Lebarbenchon, R., Camberlein, E., Di Carlo, D., Gaultier, C., Deleforge, A., & Bertin, N. (2018). Evaluation of an open-source implementation of the SRP-PHAT algorithm within the 2018 LOCATA challenge. arXiv preprint arXiv:1812.05901.
Do, H., & Silverman, H. F. (2007, October). A fast microphone array SRP-PHAT source location implementation using coarse-to-fine region contraction (CFRC). In 2007 IEEE workshop on applications of signal processing to audio and acoustics (pp. 295-298). IEEE.
Birchfield, S. T. (2004, September). A unifying framework for acoustic localization. In 2004 12th European Signal Processing Conference (pp. 1127-1130). IEEE.
Wang, D., & Brown, G. J. (2006). Computational auditory scene analysis: Principles, algorithms, and applications. Wiley-IEEE press.
Hu, J. S., Tsai, C. M., Chan, C. Y., & Chang, Y. J. (2011, May). Geometrical arrangement of microphone array for accuracy enhancement in sound source localization. In 2011 8th Asian Control Conference (ASCC) (pp. 299-304). IEEE.
Huang, J., Supaongprapa, T., Terakura, I., Wang, F., Ohnishi, N., & Sugie, N. (1999). A model-based sound localization system and its application to robot navigation. Robotics and autonomous systems, 27(4), 199-209.
Dmochowski, J. P., & Benesty, J. (2010). Steered beamforming approaches for acoustic source localization. In Speech processing in modern communication (pp. 307-337). Springer, Berlin, Heidelberg.
Nishiura, T., Yamada, T., Nakamura, S., & Shikano, K. (2000, June). Localization of multiple sound sources based on a CSP analysis with a microphone array. In 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (cat. no. 00ch37100) (Vol. 2, pp. II1053-II1056). IEEE.
Ho, K. C., Lu, X., & Kovavisaruch, L. O. (2007). Source localization using TDOA and FDOA measurements in the presence of receiver location errors: Analysis and solution. IEEE Transactions on Signal Processing, 55(2), 684-696.