Music.Me: A Personalised Music Recommendation System for Wellbeing
Why did you apply for this internship?
I applied for the N8 CIR 2025 RSE undergraduate internship because I am passionate about machine learning and its potential to drive innovation in research. This internship offers a unique opportunity to work on cutting-edge research projects in machine learning, digital health, or digital humanities, aligning perfectly with my academic background and career aspirations. I am particularly excited about the chance to collaborate with experienced researchers and RSE mentors, learning from their expertise while contributing to impactful projects.
What do you hope to gain in completing this project?
Through this internship, I hope to deepen my understanding of advanced machine learning techniques and their practical applications in research. I am eager to learn best practices in research software engineering, including software design, testing, and optimization for large-scale data. Additionally, I aim to gain experience in collaborative development, version control, and effectively communicating technical concepts within a research team. I also look forward to exploring the interdisciplinary aspects of digital health or digital humanities, depending on the project, to broaden my perspective on how machine learning can address real-world challenges.
Project Overview
This project enhances Music.Me, a personalised music recommendation platform, by incorporating emotion recognition into the recommendation process. While most systems rely only on listening history, we explored how music influences emotions and well-being by predicting two key affective dimensions: arousal (energy) and valence (positivity). Our approach was inspired by the 2025 paper “Towards Unified Music Emotion Recognition across Dimensional and Categorical Models”. We replicated and fine-tuned their multitask learning framework, adding additional datasets to improve robustness. Audio features such as spectrograms, embeddings, and chord progressions were used, with experiments also planned for OpenSMILE and Essentia.
What were the key results of your research project?
Results show that the unified model achieves strong predictive performance, outperforming baseline methods. This paves the way for recommendations tailored not only to user preferences but also to their emotional state and daily activities, supporting music’s role in everyday well-being.
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How do you feel you have benefited from completing this internship, and has it made you consider future career paths?
Taking part in the N8CIR Internship has given me hands-on experience in applying machine learning to a real-world challenge, strengthening both my technical and research skills. It also helped me develop confidence in communicating complex ideas to a broad audience. This experience has confirmed my interest in pursuing a career in AI research, particularly in projects that connect technology with human well-being.