librosa Audio Processing Engine
Relevant source files
The following files were used as context for generating this wiki page:
The librosa Audio Processing Engine serves as the core analytical component of the MoodTune application. It is integrated within a FastAPI Python backend, which is hosted on Railway, to perform high-level feature extraction from audio signals. The engine's primary responsibility is to analyze uploaded audio files or catalog tracks to determine musical characteristics such as tempo, energy, and texture, ultimately translating these technical metrics into human-readable mood classifications.
Within the broader architecture, the engine acts as the heavy-lifting service for the Flutter-based mobile interface. When a user initiates an analysis—whether through direct file upload or by selecting a track from the Jamendo catalog—the mobile app communicates with this engine via RESTful API endpoints. The results generated by librosa are then mapped to one of four visual mood identities: Upbeat, Serene, Charged, or Reflective.
Sources: README.md:9-16, README.md:46-52, CLAUDE.md:7-10
Audio Feature Extraction & Analysis Logic
The engine utilizes the librosa library to process raw audio data and extract specific acoustic features. These features are quantified to generate a "primary mood label" and a corresponding confidence score. The analysis typically takes approximately 30 seconds to complete for a standard audio file.
Key Analytical Metrics
The engine focuses on four primary audio dimensions to determine the mood:
- Tempo: The rhythmic speed or pace of the music.
- Energy: The perceived intensity and activity level.
- Brightness: The spectral content related to higher frequencies.
- Texture: The overall quality and complexity of the sound.
Sources: README.md:10-14, README.md:21-23
Analysis Workflow
The following diagram illustrates the data flow from the mobile application through the FastAPI backend to the librosa processing layer:
The diagram shows the transition from raw audio data to structured mood data. Sources: README.md:46-52, CLAUDE.md:155-161
API Surface & Integration
The librosa engine is exposed through specific endpoints in the AnalysisRemoteDataSource and AudioUploadRemoteDataSource within the Flutter application's data layer. These endpoints facilitate both direct file uploads and reference-based track analysis.
Integration Endpoints
| Endpoint | Method | Source Class | Description |
|---|---|---|---|
/audio-upload/analyze | POST | AudioUploadRemoteDataSource | Accepts multipart audio files (MP3, M4A, FLAC, WAV, OGG) up to 15 MB for analysis. |
/song/analyze/{trackId} | POST | AnalysisRemoteDataSource | Triggers analysis for a specific track ID from the Jamendo or Spotify catalogs. |
/analysis/analyze/{playlistId} | POST | AnalysisRemoteDataSource | Performs batch analysis on songs within a specified playlist. |
Sources: README.md:21-22, CLAUDE.md:143-152, CLAUDE.md:158-160
Processing Configuration
The connection between the mobile frontend and the audio engine is configured with specific timeout parameters to accommodate the computational intensity of librosa analysis.
- Connect Timeout: 30 seconds.
- Receive/Send Timeout: 90 seconds (specifically for
AudioUploadRemoteDataSource).
Sources: CLAUDE.md:143-146
Data Models & Output
The librosa engine returns a structured result that the mobile application consumes to update the UI state. The output includes technical scores and descriptive metadata.
Confidence Scoring System
The engine categorizes its findings based on the mathematical certainty of the mood match:
| Match Category | Threshold |
|---|---|
| Strong Match | ≥ 75% |
| Good Match | 50–74% |
| Possible Match | < 50% |
Sources: README.md:29-31
Output Structure
The result of an analysis cycle includes:
- Primary Mood Label: One of the four core identities (Upbeat, Serene, Charged, Reflective).
- Confidence Score: A percentage representing the engine's certainty.
- Descriptors: Specific tags describing the track's characteristics.
- Plain-Language Explanation: A summary explaining the musical reasoning behind the result.
Sources: README.md:12-15, CLAUDE.md:118-121
Conclusion
The librosa Audio Processing Engine is the foundational intelligence of MoodTune, providing the heavy-duty signal processing required to turn audio into emotional insights. By offloading these complex Python-based calculations to a FastAPI backend on Railway, the system ensures that the mobile application remains responsive while providing high-fidelity acoustic analysis. The engine's ability to process various formats and provide detailed confidence scoring allows for a transparent and engaging user experience across both guest and authenticated sessions.
Sources: README.md:9-16, CLAUDE.md:7-10