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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 丁宗蘇 | zh_TW |
| dc.contributor.advisor | Tzung-Su Ding | en |
| dc.contributor.author | 陳明芫 | zh_TW |
| dc.contributor.author | Ming-Yuan Chen | en |
| dc.date.accessioned | 2023-08-08T16:09:43Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-08 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-07-13 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88070 | - |
| dc.description.abstract | 被動式聲音監測是近年來備受矚目的生態研究方式，研究者透過此方法可以更有效率的收集聲景資料。隨著科技進步，人工智慧在聲景分析上獲得更廣泛的運用。以卷積式神經網路為基礎的SILIC是臺灣第一個開放使用的多物種自動辨識工具。本研究試圖運用SILIC探索大安森林公園的鳥類聲景，評估該工具在干擾較大的都市環境中的表現，並以其產出之標記檢驗主流的聲景生態學假說，探討噪音、天氣、時間、空間位置對鳥類發聲頻度的影響。人工驗證顯示非目標物種的干擾是SILIC辨識錯誤的主因，另一個主要干擾源則是人造音。當精確度為70%時，通過篩選的物種召回率介於0.125-1，平均為0.586；F1分數介於0.212-0.824，平均為0.586；平均精確度介於0.378-0.945，平均為0.695。噪音作為都市聲景的重要元素，對鳥類的發聲頻度產生顯著的影響，並受到週間/週末的影響。道路距離除了影響環境噪音強度，也可能影響鳥類的棲地偏好。大安森林公園的鳥類在晨間及上午發聲頻度較高。氣溫與發聲頻度正相關，且受月份影響較大，可看出鳥類發聲頻度的季節變化。降水量與最大陣風風速則反映天氣的影響。個別物種發聲頻度的變化趨勢與整體聲景大致類似，物種對各項環境因子的敏感度不同造成發聲頻度的種間差異。SILIC對高度噪音干擾的聲景資料辨識仍有良好的表現水準，其產生的標記適用於更深入的聲景研究；未來若能持續訓練模型，提升其辨識表現，應可對未來的聲景生態研究助益匪淺。 | zh_TW |
| dc.description.abstract | Passive acoustic monitoring has been a popular tool for recent ecological research, which collects soundscape data more effectively. Artificial intelligence has gained broader applications in soundscape analysis. SILIC, based on convolutional neural networks, is the first open-source multi-species identification tool in Taiwan. This study was aimed to explore bird soundscape in Daan Park using SILIC, evaluate the performance of SILIC in highly-disturbed urban environments, and use the generated labels to validate mainstream hypotheses in soundscape ecology. This study investigated the influences of noise, weather, time, and spatial location on bird vocalization frequency. The main sources of misidentification were sound form non-target species, followed by anthrophonies. With a precision threshold of 70 %, the recall for qualified species ranged from 0.125 to 1, with an average of 0.586; the F1 scores ranged from 0.212 to 0.824, with an average of 0.586; and the AP ranged from 0.378 to 0.945, with an average of 0.695. Noise, as an important element of urban soundscape, had significant impact on bird vocalization frequency, and was negatively correlated with distance to road, a factor not only affected noise amplitude, but also related to bird habitat preferences. Birds in Daan Park tended to vocalize more in morning hours. Temperature was positively correlated with bird vocalization frequency and was mainly affected by months, indicating seasonal variations. Rainfall and maximum gust wind speed were the main weather factors affecting bird vocalization frequency. The effect of environmental factors on individual species' vocalization frequencies were generally similar to the overall soundscape. The differences in vocalization frequencies among species were caused by their sensitivity to different environmental factors. SILIC maintains a certain level of performance in highly disturbed soundscape, the generated labels are applicable to further soundscape research. Continuous training of SILIC shall benefit soundscape ecology research in future. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-08T16:09:43Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-08T16:09:43Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 謝誌………………………………………………………………...………….… i
摘要…………………………………………………………………………...… ii Abstract …………………………………………………………...…………… iii 目錄………………………………………………………………...…………… v 前言………………………………………………………………...…………… 1 一、 聲景與聲景生態學…………………………………...………………… 1 二、 聲景生態學的近年發展…………………………...…………………… 3 三、 研究目的…………………………………...…………………………… 6 材料方法……………………………………………………………………...… 7 一、 研究地點…………………………………...…………………………… 7 二、 錄音資料…………………………………...…………………………… 7 三、 噪音強度…………………………………...…………………………… 8 四、 氣象資料…………………………………...………………………....… 9 五、 地理資訊資料……………………………...………………..……..…… 9 六、 SILIC人工智慧物種辨識………………....……………………..…..… 9 七、 SILIC表現評估…………………….……...…………..……………… 10 八、 統計分析…………………………………...……………………..…… 12 結果………………………………………………………………………...….. 13 一、 SILIC人工智慧物種辨識………………....……………………..…… 13 二、 鳥類發聲與噪音的時空分布………………....………………….…… 14 三、 鳥類發聲頻度與環境因子之間的關係………………………….…… 16 討論……………………………………………………..…………………...… 19 一、 SILIC綜合表現………………....……………………..……………… 19 二、 環境因子對鳥類總體聲景的影響………………....…………….…… 22 結論…………………………………………………………………...….……. 25 參考文獻……………………………………………………...……………..… 26 圖………………………………………………………………………...…….. 34 表……………………………………………………………………….......….. 55 附錄………………………………………………………………………...….. 69 | - |
| dc.language.iso | zh_TW | - |
| dc.title | 以人工智慧物種辨識工具探索大安森林公園的鳥類聲景 | zh_TW |
| dc.title | Exploring the Bird Soundscape of Daan Park Using AI-powered Species Detection and Identification Tool | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 許富雄;蔡若詩;林瑞興;端木茂甯 | zh_TW |
| dc.contributor.oralexamcommittee | Fu-Hsiung Hsu;Juo-Shi Tsai;Ruey-Shing Lin;Mao-Ning Tuanmu | en |
| dc.subject.keyword | 被動式聲音監測,物種自動辨識,模型評估,聲景生態學, | zh_TW |
| dc.subject.keyword | passive acoustic monitoring,automatic species identification,model evaluation,soundscape ecology, | en |
| dc.relation.page | 80 | - |
| dc.identifier.doi | 10.6342/NTU202301532 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2023-07-14 | - |
| dc.contributor.author-college | 生物資源暨農學院 | - |
| dc.contributor.author-dept | 森林環境暨資源學系 | - |
| 顯示於系所單位： | 森林環境暨資源學系 | |
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