Nornirx
Nornirx is a mood-based music recommendation app designed to connect users with the right music for every emotional state. By leveraging machine learning, Nornirx creates personalized playlists that align with users' current moods, making each listening session feel more meaningful. Our goal was to build an app that moves beyond traditional genre or artist recommendations, providing a unique, emotionally connected experience for users.
Year
2024
Duration
3 Months
Domain
Mental Health
Platform
Mobile
Challenge
Many music recommendation apps struggle to align with a user’s mood, often relying solely on genre or artist-based algorithms that fall short of true personalization. This lack of emotional connection leaves users disengaged, limiting the impact of their music experience.
Solution
Nornirx addresses this gap by using machine learning to generate mood-based playlists tailored to each user’s emotional state. By clustering songs based on previous listening patterns, Nornirx creates a personalized, responsive experience that adapts to how users feel, fostering a deeper connection with the music they love.
1
The Spark of an Idea
It all started with a simple but powerful idea among our team: what if a music app could match a user’s emotional state and play exactly what they need at that moment? We had all experienced the satisfaction of hearing a song that unexpectedly resonated with our mood and wanted to replicate that feeling through intentional design. This shared experience led us to develop Nornirx—a music app that goes beyond basic recommendations, delivering a listening experience that is both personal and emotionally connected.
Understanding the Problem
Through initial research and conversations with users, we identified a common desire: users want music that reflects their emotions in real time. The primary pain points included a lack of mood-based recommendations and low engagement, as traditional algorithms failed to adapt to emotional contexts. Nornirx was designed to address these gaps, offering a personalized, mood-based solution to help users feel truly understood by their music app.
Approach
User Research and Personas
Conducted surveys and interviews to understand users’ emotional connection with music.
Identified a need for mood-based music recommendations to enhance user experience.
Developed personas focusing on users’ varying emotional states and listening habits.
Defined user scenarios for mood-driven playlists and personalized music journeys.
Technology Integration (ML and Clustering)
Used k-means clustering to classify songs based on mood and emotional tone.
Integrated machine learning to generate mood-based playlists tailored to users’ current feelings.
Collaborated with ML experts to ensure accurate mood mapping for music selections.
Optimized algorithms for real-time playlist adjustments based on user input.
Visual and Interaction Design
Designed a clean, immersive interface with mood-based color themes.
Focused on simple navigation to keep users engaged and enhance emotional connection.
Incorporated smooth transitions to create a cohesive experience across moods.
Refined visuals and interactions based on user feedback for an intuitive flow.
Feature Matrix
Primary Persona
Chris, 25
A marketing professional who wants a music app that can match his emotional states, making each listening experience feel personal and supportive. Chris listens to music based on his moods, whether he’s relaxing after work, focusing during a project, or unwinding on the weekends. He values an app that understands these different moments and adapts to them seamlessly.
Empathy
Chris often feels disconnected from typical music recommendations, as they don’t reflect his fluctuating emotions. He needs an app that can intuitively adapt to his changing moods, providing a more tailored and emotionally supportive experience.
User Journey
From the moment Chris opens Nornirx, he’s guided to select his mood, which sets the tone for his playlist. As he uses the app, he can view his mood history and see how his listening patterns reflect his emotional journey. This experience keeps him engaged, as he finds music that resonates with his current state, helping him relax, focus, or recharge as needed.
Secondary Persona
Claire, 30
A freelance graphic designer who relies on music to support her creative workflow and manage stress. Claire prefers ambient and electronic music genres that suit her creative process but also enjoys switching to uplifting tunes when she needs an energy boost. She seeks an app that provides consistent mood alignment to enhance her productivity and creativity.
Empathy
Claire struggles to find an app that consistently aligns music with her creative needs, making it hard to stay in a productive flow. She needs a platform that not only provides music suited to her focus but also offers an easy transition to more energetic tracks when her mood changes.
User Journey
When Claire opens Nornirx, she’s prompted to choose her current mood, which tailors the playlist to her immediate needs. As she works, she can adjust the mood selection to match her energy, whether she’s seeking calm or a boost. The seamless interface and personalized playlists keep her focused and enhance her creative workflow.
Key Features
Mood-Based Playlists
Leveraging machine learning, playlists adapt to the user’s current mood, offering a deeply personalized listening experience.
Daily Mood Tracker
Allows users to track their listening habits according to mood, creating a visual journey that helps them see patterns in their music preferences over time.
Intuitive Interface
A clean, minimalistic design keeps the user’s focus on selecting moods and discovering new music without any unnecessary distractions.
Seamless Mood Transitions
The app’s color scheme and visual elements change based on the selected mood, providing an immersive, engaging experience that aligns visually with the user’s emotional state.
Primary User Flow
Low Fidelity Design
2
From Concept to Prototype
As we moved forward with Nornirx, our focus was on simplicity and mood alignment. Initial prototypes showcased the core features—mood-based playlists, personalized trackers, and gamified rewards. Feedback from our early testing group of college peers provided valuable insights. Users loved the mood-based recommendations but suggested further simplifying the flow and making the visuals more immersive. Based on this input, we streamlined the user flow and incorporated vibrant, mood-based color schemes, bringing Nornirx closer to the seamless, engaging experience we envisioned.
High Fidelity Design
User Feedback and Results
Feedback on the high-fidelity prototype came from a small group of college peers, who appreciated the app’s mood-based recommendations and intuitive design. The mood tracker emerged as a favorite feature, with users noting how it provided a unique, personal touch to their listening experience. Although still a prototype, Nornirx demonstrated its potential to create an emotionally connected music experience.
3
Refining Through Feedback
Reflecting on our journey, we recognized that each round of feedback was instrumental in shaping Nornirx into a more refined product. The iterative process revealed critical insights that allowed us to enhance user flow, visual appeal, and overall engagement. The result was a prototype that showcased our concept’s potential and highlighted how design and machine learning could come together to create a truly emotionally intelligent platform.
Screenshots
4
A Vision for the Future
Looking ahead, Nornirx has the potential to expand beyond mood-based playlists into a full platform that bridges music and emotional wellness. Future updates could include more advanced mood-detection algorithms, social features for shared playlists, and broader music styles to match specific emotional states. By creating meaningful, mood-aligned music experiences, Nornirx is setting the stage to become an app that doesn’t just deliver songs but offers an emotional connection through music.
5
Key Learnings
Working on NornirX deepened my understanding of the UX design process, particularly in creating low-fidelity designs and defining user personas to ensure a user-centered approach. Additionally, I honed my skills in using Auto Layout, which streamlined my design workflow and enhanced my efficiency in creating scalable and consistent interfaces.
