---
# System prepended metadata

title: Soundscape Viewer使用說明
tags: [soundscape viewer, ' tutorial', ' ecoacoustics']

---

---
title: Soundscape Viewer使用說明
description: Introduce how to use the Soundscape Viewer (Python version)
image: https://lh3.googleusercontent.com/pw/ACtC-3f8P5a_KnZTV4d_FC0AnHmpeRl2lyP0CpBc3p_bEFDwTJKq71qpennAZCryOO154X-J__HXPdeYs74AvttICk1G6tJqnsN1lj_THftx71vyaVqlZD-haoCco0pMzU5FguN3L0qQtv9WE8vR1J574SW5=w582-h556-no?authuser=0
tags: soundscape viewer, tutorial, ecoacoustics
disqus: hackmd
---

Soundscape Viewer
===
![logo](https://lh3.googleusercontent.com/pw/ACtC-3f8P5a_KnZTV4d_FC0AnHmpeRl2lyP0CpBc3p_bEFDwTJKq71qpennAZCryOO154X-J__HXPdeYs74AvttICk1G6tJqnsN1lj_THftx71vyaVqlZD-haoCco0pMzU5FguN3L0qQtv9WE8vR1J574SW5=w582-h556-no?authuser=0 =200x)

本文件為 Python 版本的 Soundscape Viewer 使用說明，Soundscape Viewer 為中央研究院生物多樣性研究中心，[海洋生態聲學與資訊實驗室](https://sites.google.com/view/marine-ecoacoustics)所開發的聲景訊息擷取 (Soundscape information retrieval) 工具，歡迎有興趣的朋友前往 [Github](https://github.com/schonkopf/soundscape_IR) 下載原始碼，也歡迎與我們聯繫合作。

:::info
除了本文件之外，也可以透過 [Colab Notebook](https://colab.research.google.com/drive/1Bod1SW8YtVgFWPO4w8WFGORmku-MPCL-#scrollTo=8F6ttwylWUdY) 來體驗 Soundscape Viewer 的各項功能。
:::

- [回到首頁](/@meil/soundscape_viewer)

使用教學
---
- [為長期時頻譜圖準備聲音資料](/Eyc9BJ-BT6e5KrFBa3Nknw)
- [製作長期時頻譜圖](/pR5gtceDS_WyPBVH7ObP9g)
- [檢視長期時頻譜圖](/USC8plCtSniVZRtaVUIrFw)
- 分析長時間錄音的頻譜特性
- [在長期時頻譜圖上執行聲源分離](/ZuOpr_95RuuJg7vUJdFiaA)
- [在長期時頻譜圖上進行群聚分析](/1xMyuesXQnaZQQjl7AKV7A)
- [觀察聲音波形與頻譜](/2TbzGo3uR52dvDjfLRV41w)

參考文獻
---
**長期時頻譜圖**
1. [Merchant ND, Fristrup KM, Johnson MP, Tyack PL, Witt MJ, Blondel P, Parks SE (2015) Measuring acoustic habitats. Methods in Ecology and Evolution 6: 257–265.](https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12330)

**聲源分離**
1. [Lin TH, Tsao Y (2019) Source separation in ecoacoustics: A roadmap toward versatile soundscape information retrieval. Remote Sensing in Ecology and Conservation: in press](https://doi.org/10.1002/rse2.141)
2. [Lin TH, Fang SH, Tsao Y (2017) Improving biodiversity assessment via unsupervised separation of biological sounds from long-duration recordings. Scientific Reports 7: 4547](https://www.nature.com/articles/s41598-017-04790-7)

**聲音聚類分析**
1. [Phillips YF, Towsey M, Roe P (2018) Revealing the ecological content of long-540 duration audio-recordings of the environment through clustering and 541 visualisation. PLOS ONE 13(3):e0193345](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193345)

**生態監測應用**
1. [Lin TH, Yang HT, Huang JM, Yao CJ, Lien YS, Wang PJ, Hu FY (2019) Evaluating changes in the marine soundscape of an offshore wind farm via the machine learning-based source separation. Underwater Technology 2019](https://ieeexplore.ieee.org/document/8734295)
2. [Lin TH, Tsao Y (2018) Listening to the deep: Exploring marine soundscape variability by information retrieval techniques. OCEANS ’18 Kobe](https://ieeexplore.ieee.org/document/8559307)
3. [Lin TH, Tsao Y, Akamatsu T (2018) Comparison of passive acoustic soniferous fish monitoring with supervised and unsupervised approaches. Journal of the Acoustical Society of America Express Letters 143: EL278](https://asa.scitation.org/doi/10.1121/1.5034169)