Project Abstract

The Weather and Music Alarm Clock project introduces an innovative approach to the traditional wake-up experience by seamlessly integrating weather forecast data with personalized music selection. Traditional alarm clocks often lack customization and fail to provide meaningful context, leading to a mundane and repetitive wake-up routine. This project aims to address these limitations by creating a dynamic alarm clock application that not only wakes users up at the desired time but also sets the tone for the day ahead through curated music tailored to the weather forecast.The project explores the correlation between weather conditions and musical preferences, leveraging insights from psychology, meteorology, and musicology to inform the design of custom algorithms for music selection. These algorithms analyze real-time weather forecast data and user preferences to recommend music tracks that align with the prevailing weather conditions, ensuring a seamless and enjoyable wake-up experience for users.Key features of the application include a user-friendly interface design, integration with external services such as Weather APIs and music streaming platforms, and personalized music recommendations based on user feedback and interaction data. Through iterative development, testing, and user feedback, the project aims to create a robust and user-friendly alarm clock application that enhances the wake-up experience through personalized music selection and contextual relevance.Overall, the Weather and Music Alarm Clock project represents a novel fusion of technology, music, and meteorology, offering users a unique and engaging wake-up experience that transcends the limitations of traditional alarm clocks. By leveraging weather forecast data and personalized music selection, the application sets a new standard for personalized wake-up experiences, enriching users’ mornings and setting a positive tone for the day ahead.

Keywords: Mood-based music selection, Human-computer interaction, Iterative development

 Conference Details

Session: Presentation Stream 4 at Presentation Slot 8

Location: GH001 at Tuesday 7th 13:30 – 17:00

Markers: Neil Carter, Lu Zhang

Course: MSc Computer Science, Masters PG

Future Plans: I’m looking for an industry placement