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Commit 0a4b08b4 authored by Venkatesh Potluri's avatar Venkatesh Potluri
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updated spec for hw2 on sound recognition.

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6 merge requests!12Accessibility website jen summer work,!9Q access 24 sp,!8Q access 24 sp,!5Latest update from Spring 2023,!4Latest update after pmp class,!3Updated content for week 3 and week 4 (week 4 still needs work)
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title: Teach Machines to Recognise Sounds
code: as4
assigned: April 29, 2021
assigned: April 26, 2021
due: May 5, 2021
revised: March 31, 2021
revised: April 25, 2021
objective: Teach machines to recognise non-speech sounds that occur around you.
objective: Teach machines to recognise non-speech sounds that occur around you and visualize these recognised sounds.
---
## Timeline
## Timeline (tentative)
- <strong>in-class check-in</strong> on May 3, 2021.
- <strong> final presentations</strong> in class on May 5, 2021
- <strong> final deliverable</strong> in class on May 5, 2021
Full spec coming soon!
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In this homework, you will do two things:
1. Use a Jupytor notebook (provided to you on canvas) that contains code to train a machine learning model to recognise sounds and build visualizations to display the recognised sounds. We will post the notebook on canvas, and you are strongly encouraged to host the notebook on Google Collab. login to your UW CSE provided google account, upload the notebook on your drive, and open the notebook in google colab on your browser. [Here is a getting started tutorial on colab](https://colab.research.google.com/notebooks/intro.ipynb#).
2. caption a video, read related papers, and reflect on your experience captioning these videos. You should submit a caption file in a standard format like wsb or srt. Please review some helpful links in the deliverables section of this page to create a caption file in these standard formats. You are allowed to start with an AI-based service to generate captions, however, you should actively fixe eronious captions if you are using the AI-based service.
## Deliverables
You will submit the following
* a completed notebook or a link to the completed notebook with sufficient permissions to the staff to access, run and evaluate it.
* link to, or the original file of the video you chose to caption.
* a link to, or the original caption file in a standard format like SBV or SRT. [this blogpost from rev.com](https://www.rev.com/blog/close-caption-file-format-guide-for-youtube-vimeo-netflix-and-more) is a good summary of different formats. once you have captions, making a caption file in one of these standard formats should not be hard. For example, here is a [tutorial on using youtube to add captions to your videos](https://support.google.com/youtube/answer/2734796?hl=en).
We have allowed maximum flexibility to upload these deliverables to canvas. Please upload multiple files, or use the comments section to submit links. Please indicate how exactly have you submitted the deliverables using the comment functionality of canvas once you submit your work.
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