本文目標是建立台灣近海魚類聲音資料庫，以儲存聲音樣本的詳細資訊，除
此之外還可提供一個資訊交流平台，增加研究人員在生物行為、物種辨認以及定
位追蹤等研究的便利性。在進行資料上傳之前，資料庫會先對聲音樣本進行品質
分析，以訊雜比作為品質分析依據，提供使用者在選擇聲音樣本的一項參考。由
於資料來源有實際海域錄音與水族箱錄音兩大類，為了正確框選出魚類聲音訊
號，本文使用兩種端點偵測法進行魚類聲音偵測，當聲音樣本是實際海域錄音
時，採用時域端點法進行偵測，設定音框為0.5 秒、重疊率為50%進行聲音樣本
切割，計算每個音框內的振幅平方和，呈現聲音樣本的音框能量分佈，最後在進
行魚類聲音訊號萃取時，設定門檻值為音框能量圖的中位數加上一個標準差。當
聲音樣本為水族箱錄音時，採用頻率端點法進行萃取，以音框為0.5 秒、重疊率
50%切割聲音樣本，再將每個音框進行頻譜轉換，計算單一音框下各頻率所佔能
量比例，依據所佔比例計算聲音樣本的信息熵分佈，設定門檻值為聲音樣本信息
熵的中位數加上一個標準差，進行魚類訊號萃取。最後配合偵測結果計算魚類訊
號的平均功率以及背景值的平均功率，得到聲音樣本的訊雜比。本資料庫所使用
的軟體為MySQL 與PHP，採用三層式架構進行資料庫建立，為了降低資料的儲
存容量同時保持資料完整性，以關聯模式作為資料儲存的格式，其方法是用實體
關聯圖繪製資料間的相關性，再將實體關聯圖轉換成關聯綱目，最後進行第一正
規化、第二正規化、第三正規化，以確保關聯綱目在資料異動時不會發生錯誤。
本資料庫除了提供資料上傳的平台外，還提供生物名稱、錄音地點、錄音時間三
種搜尋方式與利用錄音編號進行資料比對的介面，希望增加使用者使用資料庫的
便利性與效率。

The goal of development of sound database for marine fishes in Taiwan not only preserves data, but also wants to provide a common ground of data sharing to increase the efficiency for the study of fish behavior, automatic recognition, localization, and tracking. In order to provide the sound quality in terms of signal-to-noise ratio to users, the fish sound recording will be analyzed before uploading. Because most available data were recorded either in the field or in fish tank, the fish sounds were extracted by using two different automatic detection methods. If fish sound recordings were from the field, the Time Endpoint Detection was applied by the processing a 0.5-s time frame with 50 % overlapping. Then the energy of the time frame was obtained by the sum of square of amplitude and the median of the energy plus a standard deviation was established as the threshold to extract fish sounds. If the recording was made in the fish tank, the Frequency Endpoint Detection was applied by 0.5-s time frame with 50 % overlapping. Then each time frame will be transformed into spectrum and the energy ratio of each frequency will be calculated from the spectrum. Finally the information entropy was obtained from the energy ratio and the detection threshold was set on standard deviation above the median of the information entropy. From two different automatic detection methods, the sound quality was presented in the signal-to-noise ratio, which was the average power of signal divided by average power of the background noise. The fish sound database was a 3-Tier system and developed by PHP and MySQL. In order to reduce the storage size and maintain the integrity of data, the Relational Model was applied. Firstly, the recording data were conceptually represented as Entity-Relationship Diagram(ERD). Secondly, the ERD was transformed to relational schemas. Thirdly, the schemas was normalized by first, second, and third forms. To improve the users’ efficiency the sound database provides three interfaces. One was data uploading, another was data searching according to the keyword of creature name, recording area, and recording time, the other was data comparing by recording number.

摘要 .................................................................................................... i
Abstract ............................................................................................... ii
目錄 .................................................................................................... iii
表目錄 ................................................................................................ vi
圖目錄 ................................................................................................ vii
第一章 緒論 ...................................................................................... 1
1.1 研究背景 ................................................................................. 1
1.1.1 水中聲學發展................................................................ 1
1.1.2 聲納介紹........................................................................ 2
1.2 海洋生物之聲納運用 ............................................................. 3
1.2.1 生物估計與定位 .......................................................... 3
1.2.2 聲音行為關係與魚種辨識............................................ 4
1.3 生物聲音資料庫....................................................................... 5
1.3.1 Tierstimmenarchiv.......................................................... 5
1.3.2 The British Sound Archive ............................................. 6
1.3.3 Macaulay Library............................................................ 7
1.3.4 台灣大學數位動物博物館............................................ 7
1.3.5 台灣魚類資料庫............................................................ 8
1.4 研究目的................................................................................... 9
第二章 聲音品質分析........................................................................ 10
2.1 自動化聲音萃取....................................................................... 10
2.2 時域端點偵測法....................................................................... 11
2.2.1 時域端點偵測法之參數設定........................................ 12
2.2.2 時域端點偵測法之流程................................................ 13
2.2.3 門檻值設定.................................................................... 14
2.3 頻率端點偵測法…................................................................... 16
2.3.1 頻率端點偵測法之流程................................................ 17
　2.4 聲音品質分析........................................................................... 17
2.5 實驗材料與結果....................................................................... 19
2.5.1 時域端點偵測法偵測結果............................................ 19
2.5.2 頻率端點偵測法偵測結果............................................ 22
　2.6 時域端點法與頻率端點法的差異........................................... 25
第三章 魚類聲音資料庫建立 .......................................................... 28
3.1 資料庫理論............................................................................... 28
3.2 資料庫系統架構....................................................................... 29
3.3 資料庫管理系統…................................................................... 31
3.3.1 關聯模式........................................................................ 33
3.3.2 概念塑模........................................................................ 34
3.3.3 邏輯塑模........................................................................ 38
3.3.4 實體塑模........................................................................ 42
3.4 魚類聲音資料庫架構............................................................... 43
3.5 台灣近海魚類聲音資料庫....................................................... 48
3.5.1 資料上傳........................................................................ 49
3.5.2 搜尋方法........................................................................ 51
3.5.3 資料比對...... ................................................................. 55
第四章 結論........................................................................................ 56
4.1結果探討…................................................................................ 56
4.2未來發展.................................................................................... 57

[1]劉金源，水中聲學—水聲系統之基本操作原理，國立編譯館，2001。
[2]X. Lurton, An introduction to Underwater Acoustics, Springer, pp. 4-9, 2005.
[3]H. Medwin, Sound in the Sea, Cambridge University Press, pp. 375-376, 2005.
[4]D. V. Holliday and R. E. Pieper, “Volume scattering strengths and plankton distribution at acoustic frequencies between 0.5 and 3 MHz”, J. Acoust. Soc. Am. Volume 64, Issue S1, pp. S94-S94, 1978.
[5]T. K. Stanton, ”Density estimates of biological sound Scatters using sonar echo peak PDFs”, J. Acoust. Soc. Am. Volume 78, Issue 5, pp. 1868-1873, 1985.
[6]W. A. Watkins and W. E. Schevil, “Sound Source Location by Arrival-Times on a Non-Rigid Three Dimensional Hydrophone Array”, Deep-Sea Research(15), pp. 691~706, 1972.
[7]A. Thode, “Tracking sperm whale(Physeter macrocephalus) div profiles using a towed passive acoustic array”, J. Acoust. Soc. Am. Volume 116, Issue 1, pp. 245-253, 2004.
[8]A. A. Myrberg, “Sound communication and interception of fishes”, In: Hearing and Sound Communication in Fishes, Springer-Verlag, New York, pp. 395-452.
[9]P. O. Thompson, W. C. Cummings, and S. J. Ha, ”Sounds, source levels, and associated behavior of humpback whales, Southeast Alaska”, J. Acoust. Soc. Am. Volume 80, Issue 3, pp. 735-740, 1986.
[10]D. A. Mann and P. S. Lobel, “Propagation of damselfish (Pomacentridae) courtship sounds”, J. Acoust. Soc. Am. Volume 101, Issue 6, pp. 3783-3791, 1997.
[11]D. A. Mann and P. S. Lobel, “Acoustic behavior of the damselfish Dascyllus albisella: behavioral and geographic variation”, Environmental Biology of Fishes　Volume 51, Number 4, pp. 421-428, 2004 .
[12]C. Niezrecki, R. Phillips, M. Meyer, and D. O. Beusse, “Acoustic Detection of Manatee Vocalization”, J. Acoust. Soc. Am. Volume 114, Issue 3, pp. 1640-1647, 2003.
[13]陳建宏，莎姆金鱗魚、赤松毬魚、與刺棘金鱗魚(金鱗魚科)擾動聲音辨識，國立中山大學海下技術研究所碩士輪文，2006。
[14]蔡英薇，金鱗魚擾動聲音辨識方法之效能評估，國立中山大學海下技術研究所碩士論文，2007。
[15]D. K. Mellinger and C. W. Clark, ”MobySound：A reference archive for studying automatic recognition of marine mammal sound”, Applied Acoustic Volume 67, pp. 1226-1242, 2006.
[16]R. Ranft, ”Natural sound archives: past, present and future”, Annals of the Brazilian Academy of Sciences Volume 76, pp. 455-465, 2004.
[17]Tierstimmenarchiv (2009/08) http://www.tierstimmen.org/
[18]The British Sound Archive (2009/08) http://cadensa.bl.uk/uhtbin/cgisirsi/sYPaN4caP7/41430006/0/49
[19]Macaulay Library(2009/08) http://macaulaylibrary.org/index.do
[20]台灣大學數位動物博物館(2009/08) http://archive.zo.ntu.edu.tw/index.asp
[21]台灣魚類資料庫(2009/08) http://fishdb.sinica.edu.tw/chi/home.php
[22]Ｍ. D. Skowronski and M. B. Fenton, “Quantifying bat call detection performance of humans and machines”, J. Acoust. Soc. Am. Volume 125, Issue 1, pp. 513-521, 2009.
[23]L. S. Huang and C. H. Yang, “A novel approach to robust speech endpoint detection in carenvironments”, Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on, Volume 3, pp. 1751-1754.
[24]P. Clemins, M. Johnson, K. Leong, and A. Savage, “Automatic classification and speaker identification of African elephant( Loxodonta africana) vocalizations”, J. Acoust. Soc. Am. Volume 117, pp. 956-963, 2005 .
[25]P. Rickwood and A. Taylor, “Methods for automatically analyzing humpback song units”, J. Acoust. Soc. Am. Volume 123, Issue 3, pp. 1763-1772, 2008.
[26]陳松琳，以類神經為網路之語音辨識系統，國立中山大學電機工程學系研究所碩士論文，2002。
[27]C. Erbe and A. R. King, “Automatic detection of marine mammals using information entropy”, J. Acoust. Soc. Am. Volume 124, Issue 5, pp. 2833-2840, 2008.
[28]J. L. Shen, J. W. Hung and L. S. Lee, “Robust Entropy-based Endpoint Detection for Speech Recognition in Noisy Environments”, presented at the ICSLP, 1998.
[29]C. E. Shannon, “A Mathematical Theory of Communication”, Mobile Compting and Communications Review, Volume 5, pp. 3-55, 2001.
[30]黃三益，資料庫的核心理論與實務，前程文化事業有限公司，2008。
[31]陳祥輝，資料庫系統理論與實務，旗標出版股份有限公司，2008。
[32]E. F. Codd, “A Relational Model of Data for Large Shared Data Banks”, Communication of the ACM, Volume 13, Issue 6, pp. 377-387, 1970.
[33]P. S. Chen, “The entity-relationship model-toward a unified view of data”, ACM Transactions on Database System Volume 1, Issue 1, pp. 9-36, 1976.