應用於災難中人員搜救之麥克風陣列聲源定位系統

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：33

、訪客IP：3.141.7.130

姓名

王瀚綸(Han-Lun Wang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

應用於災難中人員搜救之麥克風陣列聲源定位系統
(Acoustic Source Localization System for People Search and Rescue in the Disaster)

相關論文

★ 獨立成份分析法於真實環境中聲音訊號分離之探討	★ 口腔核磁共振影像的分割與三維灰階值內插
★ 數位式氣喘尖峰氣流量監測系統設計	★ 結合人工電子耳與助聽器對中文語音辨識率的影響
★ 人工電子耳進階結合編碼策略的中文語音辨識成效模擬--結合助聽器之分析	★ 中文發聲之神經關聯性的腦功能磁振造影研究
★ 利用有限元素法建構3維的舌頭力學模型	★ 以磁振造影為基礎的立體舌頭圖譜之建構
★ 腎小管之草酸鈣濃度變化與草酸鈣結石關係之模擬研究	★ 口腔磁振影像舌頭構造之自動分割
★ 微波輸出窗電性匹配之研究	★ 以軟體為基準的助聽器模擬平台之發展-噪音消除
★ 以軟體為基準的助聽器模擬平台之發展-回饋音消除	★ 模擬人工電子耳頻道數、刺激速率與雙耳聽對噪音環境下中文語音辨識率之影響
★ 用類神經網路研究中文語音聲調產生之神經關聯性	★ 教學用電腦模擬生理系統之建構

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2024-9-1以後開放)

摘要(中)

災難的救援搜索非常仰賴搜救團隊，他們冒著高風險進入災難環境中拯救受困人員。而科技的進步，例如光學、熱能、以及聲學感測等等也可以看到應用於搜救上的發明，使得協助搜救人員能夠更有效的、更安全的執行搜救任務。但在部份環境中，部分感測器難以運作，像是在火災中因為高溫以及濃煙無法運用熱能以及光學感測，或是在深山中樹高草雜，光學感測難以運行。而從上述提到的兩種環境下，體現出聲學感測的優勢。故本篇文章便是運用聲源定位系統的理念進行搜救裝置的開發，運用Matlab軟體模擬找尋出麥克風陣列幾何的最佳方案，使其具備體積小、全方位角度定位的優勢，以此設計出具有上述功能的立體菱形陣列，結合聲源定位演算法，其中含有轉向響應功率法(Steered Response Power Phase Transform, SRP-PHAT)以及新型頻率融合對角卸載法(Diagonal Unloading Beamformer with Novel Norm Transform, DU-NORT)，設計出能夠分別適應火災以及山難兩個災難環境下的雙系統。研究中在模擬環境下建構聲學模擬空間，測試面對不同的混響環境的抗噪能力，以及在現實環境下設計此兩種災難環境，分別加入該環境會有的噪音，像是火警鈴聲與強風聲，並透過立體菱形陣列與聲源定位演算法結合的系統進行實驗。由實驗結果獲得在火災環境中，使用SRP-PHAT演算法配合自適應聲音偵測器(Adaptive Sound Detection, ASD)能夠在最低訊噪比SNR = -10dB下，僅有10°的定位誤差，且在混響環境中，使用的定位演算法能在如教室般的室內空間，對該環境空間具有混響穩健性；而在山難環境中，使用DU-NORT演算法配合遮蔽抗噪系統(Mask)，能夠在訊噪比SNR = -25dB ~ -10dB下僅有22°的定位誤差。結果呈現系統能夠在火災的濃煙以及火警鈴干擾下，或是在山難的強風吹拂的影響下，有效的協助搜救團隊進行人員搜索的任務。

摘要(英)

Search and rescue largely relies on the efforts of the search-and-rescue teams, who take high risks into disasters to rescue and search for victims. Advanced devices in technologies, such as optics, thermal, and acoustic sensors, have been applied in this field for search-and-rescue teams to operate more efficiently and safely. However, some devices are difficult to work in certain environments. For example, thermal and optical sensors cannot be fully functional in fire disaster due to high temperature and thick smoke. Optical sensors are hard to detect in mountain forests. From the two environments mentioned above, the advantages of the acoustic sensing stand out. Therefore, this thesis uses the concept of the sound source localization to develop search-and-rescue devices. In this research, we used Matlab simulation to find out the optimal solution of microphone array geometry for the advantages of small size and omnidirectional positioning. On this basis, we designed a three-dimensional diamond array, and combined it with two sound source localization algorithms, which are Diagonal Unloading Beamformer with Novel Norm Transform (DU-NORT), and Steered Response Power Phase Transform (SRP-PHAT), in two systems for fire and mountain disasters, respectively. In our experiments, we built an acoustic environment simulation to evaluate the robustness of reverberation. Also, we designed two realistic environments of fire and mountain disasters, and added noises of fire alarm and strong winds, respectively. Furthermore, we evaluated the performance of the systems combining the three-dimensional diamond array and sound source localization algorithms. The experimental results showed that using SRP-PHAT algorithm with Adaptive Sound Detector (ASD) in fire disaster can achieve only 10° localization error at the lowest signal-to-noise ratio (SNR) of -10dB, and can be applied in the indoor space of reverberation environment, such as classrooms. Using DU-NORT algorithm with noise robustness system called Mask in mountain disaster can achieve only 22° localization error at SNRs between -25 and -10dB. It demonstrated our systems can effectively assist search-and-rescue operations under the interference of thick smoke and fire alarm in fire disaster, or under the influence of strong winds in mountain disaster.

關鍵字(中)

★ 聲源定位
★ 麥克風陣列
★ 噪音穩健性
★ 災難搜救
★ 混響

關鍵字(英)

★ Sound Source Localization
★ Microphone Array
★ Noise Robustness
★ Disaster Rescue
★ Reverberation

論文目次

摘要 i
Abstract iii
目錄 v
圖目錄 viii
表目錄 x
第一章緒論 1
1.1 研究動機 1
1.2 相關研究與文獻探討 3
1.2.1波束形成技術 3
1.2.2麥克風陣列設計 6
1.2.3災難環境分析 7
1.3 研究目的 8
1.4 論文架構 9
第二章理論介紹 11
2.1 麥克風信號處理模型 11
2.2 波束形成器模型 13
2.3 聲源定位演算法 15
2.3.1轉向功率相位轉換法(Steered Response Power Phase Transform, SRP-PHAT) 15
2.3.2最小能量無失真響應(the Minimum Variance Distortionless Response, MPDR) 17
2.3.3多信號分類法 (Multiple Signal Classification, MUSIC) 19
2.3.4新型頻率融合對角消除波束形成器(Diagonal Unloading Beamformer with Novel Norm Transform, DU-NORT) 20
2.4 噪音穩健性系統 23
2.4.1 火災環境 23
2.4.2 山難環境 24
2.5 麥克風陣列幾何結構影響 27
2.6 結論 29
第三章研究方法 30
3.1硬體裝置 30
3.1.1微機電麥克風 30
3.1.2麥克風陣列 31
3.1.3數據採集器 41
3.2系統參數設定 42
3.3系統架構 42
3.4結論 43
第四章實驗結果 44
4.1實驗一、精準度量測 44
4.2實驗二、火災環境模擬實驗 47
4.3實驗三、混響環境實驗 49
4.4實驗四、山難環境模擬實驗 50
4.5討論與結論 54
4.5.1討論 54
4.5.2結論 56
第五章結論與未來展望 57
5.1結論 57
5.2未來展望 58
參考文獻 60

參考文獻

Ciric, D. G., Dordevic, A., & Licanin, M. (2013). “Analysis of Effects of Spherical Microphone Array Physical Parameters Using Simulations”. Electronics and Energetics, Volume 26, Issue 2. pp.79-85.

Huang, G., Chen, J., & Benesty, J. (2018). “On the design of differential beamformers with arbitrary planar microphone array geometry”. The Journal of the Acoustical Society of America, Volume 144, Issue 1. pp.66-70.

Hoshiba, K., Nakadai, K., Kumon, & M., Okuno H. G. (2018). “Assessment of MUSIC-Based Noise-Robust Sound Source Localization with Active Frequency Range Filtering”. Journal of Robotic and Mechatronics, Volume 30, Issue 3. pp.426-435.

Hoshiba, K., Washizaki, K., Wakabayashi, M., Ishiki, T., Kumon, M., Bando, Y., Gabriel, D., Nakadai, K., & Okuno, H. G. (2017). “Design of UAV-Embedded Microphone Array System for Sound Source Localization in Outdoor Environments”. Sensor, Volume 17, Issue11. pp.2535.

Liaquat, M. U., Munawar, H. S., Rahman, A., Qadir, Z., Kouzani, A. Z., & Parvez Mahmud, M. A. (2021). “Localization of Sound Source: A Systematic Review”. Energies, Volume 14, Issue 13. pp.3910.

Meena, S.R., & Rai, C. S. (2020). ”Effect of eigenvalue spread in noise cancellation of two sensory systems using adaptive algorithms”. Journal of Statistics and Management Systems, Volume 23, Issue 1. pp.157-169.

Nquyen, Q., Shen, G., & Choi, J. (2015). “Sound Detection and Localization in Windy Conditions for Intelligent Outdoor Security Cameras”. Circuits Systems and Signal Processing, Volume 35, Issue 1. pp.233-251.

Nakadai, K., Itoyama, K., Hoshiba, K., & Okuno, H. G. (2018). “MUSIC- BASED SOUND SOURCE LOCALIZATION AND TRACKING FOR TASK 1 AND 3”. LOCATA Challenge Workshop, a satellite event of IWAENC. September 17-20, 2018, Tokyo, Japan.

Nakamura, K., Nakadai, K., & Ince, G. (2012). “Real-time Super-resolution Sound Source Localization for Robots”. IEE/RSJ International Conference on Intelligent Robot and Systems. October 7-12, 2012. Vilamoura, Algarve, Portugal.

Nakamura, K., Nakadai, K., Asano, F., Hasegawa, Y., & Tsujino, H. (2009). “Intelligent Sound Source Localization for Dynamic Environments”. IEEE/RSJ International Conference on Intelligent Robot and Systems. October 11-15, 2009 St. Louis, USA.

Okutani, K., Yoshida, T., Nakamura, K., & Nakadai, K. (2012). “Outdoor Auditory Scene Analysis Using a Moving Microphone Array Embedded in a Quadrocopter”. IEEE/RSJ International Conference on Intelligent Robot and Systems. October 7-12, 2012. Vilamoura, Algarve, Portugal.

Ohata, T., Nakamura, K., Mizumoto, T., Taiki, T., & Nakadai, K. (2014). “Improvement in Outdoor Sound Source Detection Using a Quadrotor-Embedded Microphone Array”. IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS 2014). September 14-18, 2014, Chicaga, IL, USA.

Pavlidi, D., Griffin, A., Puigt, M., & Mouchtaris, A. (2013). “Real-Time Multiple Sound Source Localization and Counting Using a Circular Microphone Array”. IEEE Transactions on Audio, Speech, and Language Processing, Volume 21, Issue 10. pp.2193-2206.

Rathnayake, A., & Wanniarachchi, W. K. I. L. (2019). “Image Source Method Based Acoustic Simulation For 3-D Room Environment”. International Journal of Scientific & Technology Research, Volume 8, Issue 11. pp.222-228.

Ren, M., & Zou, Y. X. (2012). “A Novel Multiple Sparse Source Localization Using Triangular Pyramid Microphone Array”. IEEE Signal Processing Letters, Volume 19, Issue 2. pp.83-86.

Salvati, D., Drioli, C., & Foresti, G. L. (2020). “Diagonal Unloading Beamforming in the Spherical Harmonic Domain for Acoustic Source Localization in Reverberant Environments”. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Volume 28. pp.2001-2012.

Salvati, D., Drioli, C., & Foresti, G. L. (2014). “Incoherent Frequency Fusion for Broadband Steered Response Power Algorithms in Noisy Evironments”. IEEE Signal Processing Letters, Volume 21, Issue 5. pp.581-585.

Salvati, D., Drioli, C., & Foresti, G. L. (2018). “A Low-Complexity Robust Beamforming Using Diagonal Unloading for Acoustic Source Localization”. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Volume 26, Issue 3. pp.609-622.

Salvati, D., Drioli, C., Ferrin, G., & Foresti, G. L. (2020). “Acoustic Source Localization From Multirotor UAVs”. IEEE Transactions on Industrial Electronics, Volume 67, Issue 10. pp.8618-8628.

Sibanyoni, S. V., Ramotsoela, D. T., Silva, B. J., & Hancke, G. P. (2019). “A 2-D Acoustic Source Localization System for Drones in Search and Rescue Missions”. IEEE Sensors Journal, Volume 19, Issue 1. pp.332-341.

Wang, F., & Pan, X. (2016). “Acoustic Sources Localization in 3D Using Multiple Spherical Arrays”. Journal of Electrical Engineering and Technology, Volume 11, Issue 3. pp.759-768.

Xu, K. (2021). “How to Determine an Optimal Noise Subspace?”. Project: Signal Processing with Granular computing.

Ying, D., & Yan, Y. (2013). “Robust and Fast Localization of Single Speech Source Using a Planar Array”. IEEE Signal Processing Letters, Volume 20, Issue 9. pp.909-912.

Yamazaki, Y., Premachandra, C., & Perea, C. J. (2017). “Audio-processing-based Human Detection at Disaster Sites with Unmanned Aerical Vehicle”. IEEE Access, Volume 8. pp.101398-101405.

Zhang, B., Masahide, K., & Lim, H. (2019). “Sound Source Localization and Interaction based Human Searching Robot under Disaster Environment”. 2019 SICE International Symposium on Control Systems. Conference. Kumamoto, Japan.

姚承甫，“微機電麥克風陣列聲源定位”，中央大學電機工程學系碩士論文
，2020。

內政部消防署 – 地震應變時序 (2022/03/01)
https://www.nfa.gov.tw/cht/index.php?code=list&ids=275

中華民國山難救助協會(2022/03/01)
https://www.mtrescue.org.tw/sample-page/

內政部消防署-消防法令(2022/03/01)
https://law.nfa.gov.tw/mobile/law.aspx?LSID=FL035050

指導教授

吳炤民(Chao-Min Wu)

審核日期

2022-8-18

推文