Web scraping methods can nowadays be very helpful to automatically collect data and build large-sized datasets which can be necessary when developing machine learning algorithms. The project aims at implementing a convenient algorithm for the automatic extraction and tagging of audio data by using web scraping, crowd-sourcing and publicly available repositories.
Apply web scraping-based algorithms to extract and catalogue automatically audio data from publicly available repositories.
- Analyze audio data from open source repositories
- Apply web-scraping methods to collect datasets from publicly available repositories
- Use crowd-sourcing to catalog the audio data
- Apply machine learning algorithms to automate the process
|Project type||Seminar (optional: Master thesis)|
|ECTS||2.5, 5, 7.5, default: 5|
|Period||Winter term 2020/21|
|Presence time||Virtual seminar, working from remote|
|Useful knowledge||Python, Machine Learning|
|Work distribution||100% algorithm development|
|Med. Eng. designation||Advanced Context Recognition (ACR)|
|StudOn link||Please join|
|First Meeting||online-introduction-vorbesprechung-of-winter-term-2020-seminars, on 4th November 2020 at 16:15|
|Registration||Via StudOn, obligatory after introduction|
Up-to-date literature recommendations are provided during the lectures.
Final presentation and final report.