WorkTunes

The Project

The 'WorkTunes' program is designed to recommend the best background music for enhancing writing quality. It operates by monitoring the user’s typing behavior in real-time, tracking key performance indicators such as typos, typing speed, pauses, and the frequency of corrections or deletions. This data is analyzed by a machine learning classifier trained on a dataset that correlates these metrics with overall work efficiency. The classifier generates a score indicating the efficiency level of the user under the influence of the background music being played. This process helps users identify the music genre that most significantly boosts their writing quality, thereby enhancing their engagement and productivity during creative writing tasks. To promote this innovative tool, I have created a website, "worktunes.org," where people can learn more about the program and download it to improve their writing experience.

View this project on YouTube

Team Comments

I've always found that listening to music while engaging in various tasks helps me concentrate. The music I love alleviate boredom and even infuses joy, and others also shared my experience. Thus, I decided to undertake a project aimed at optimizing the use of music for enhanced writing efficiency.

Developing the algorithm. I used a machine learning model(clf)to predict writing quality. I need carefully decide the variables' weights included in the clf. Incorporating the algorithm into the backend server also required careful consideration. I solved the problem by learning this on YouTube.

1. Find more volunteers to try our demo and make improvements, also increase the data size for machine learning. 2. Include a more complex NLP model to relate recommended music with text type and tone. 3. Insert GPT-4 model to give a more intuitive and user-friendly feedback instead of numeric data