AI Fashion-Music Curator
is an innovative IoT and machine learning project that creates a personalized music experience by correlating clothing choices with listening habits. The system uses NFC-tagged clothing and Spotify listening history to generate custom playlists tailored to the users activities and moods
System diagram
- Clothing Data: Using RFID-RC522 sensor with a Raspberry Pi 3B+, the system reads NTAG213 tags attached to clothing items. Each scan records the item and timestamp.
- Music Data: Utilizing the Spotify API, the system fetches detailed track features including danceability, acousticness, energy, instrumentalness, valence, and more.
Data Collection
The system collects data from two primary sources:
Schematic of the RPi to RC-522 sensor
Data Analysis and Clustering
The core of the StyleSync system lies in its data analysis and clustering approach:
- Data Normalization: Track features are normalized to ensure comparability. For instance, loudness is scaled to match the 0-1 range of other features.
- Feature Selection:Based on initial analysis, less relevant features like 'speechiness' and 'liveness' were dropped to focus on more impactful attributes.
- K-means Clustering: The system uses k-means clustering to group songs into distinct mood categories. The optimal number of clusters (k=4) was determined using the elbow method.
- Principal Component Analysis (PCA): To visualize the high-dimensional data, PCA was employed, revealing clear separation between the four identified clusters.
- Cluster 0 (Study): High acousticness and instrumentalness, typically classical music.
- Cluster 1 (Happy): High valence, upbeat tracks.
- Cluster 2 (Chill): Moderate values across features, relaxed music.
- Cluster 3 (Energetic): High energy, often associated with running or workout playlists.
The four identified clusters correspond to distinct music moods:
Machine Learning Model
To predict the appropriate music mood based on clothing choices, the clustered data was exported to CSV files with their respective 'mood' labels.
Data visualisations
Future Enhancements
- Incorporate more data sources (e.g., location, time of day) for more nuanced recommendations.
- Implement more sophisticated models to capture complex relationships between clothing and music preferences.
- Develop a more intuitive user interface for real-time music recommendations.
StyleSync demonstrates the potential of combining IoT, machine learning, and music streaming technologies to create a truly personalized user experience, bridging the gap between physical fashion choices and digital music preferences.