AI Music Generation Detection
How v9.3 identifies AI-generated music from 8+ platforms
What is AI Music Detection?
v9.3's AI detection system analyzes tracks to determine whether they were created by AI music generation platforms (Suno, Udio, AIVA, etc.) or human composers and producers.
Detection Capabilities:
- Identify specific AI generation platform (Suno, Udio, AIVA, etc.)
- Provide confidence scores (0-100%)
- Calculate platform-specific risk assessments
- Detect human-created music with high accuracy
- Analyze audio features for AI indicators
Supported AI Platforms
v9.3 can detect music from 8 major AI generation platforms, each with distinct characteristics and risk profiles.
Suno - High Risk Platform
Risk Scores:
- Copyright Risk: 85%
- Legal Risk: 70%
- Ownership Risk: 90%
- Market Risk: 60%
Characteristics: Text-to-music generation, full song creation with vocals, high commercial use
Udio - High Risk Platform
Risk Scores:
- Copyright Risk: 80%
- Legal Risk: 65%
- Ownership Risk: 85%
- Market Risk: 55%
Characteristics: Advanced vocal synthesis, genre-specific generation, commercial applications
AIVA - Moderate Risk Platform
Risk Scores:
- Copyright Risk: 40%
- Legal Risk: 30%
- Ownership Risk: 45%
- Market Risk: 35%
Characteristics: Classical and cinematic focus, licensed for commercial use, clear ownership terms
Soundraw
Risk: 35% - Background music generation, royalty-free licensing
Soundful
Risk: 35% - Loop and template-based, clear licensing terms
Mubert
Risk: 45% - Generative ambient music, streaming-focused
Beatoven
Risk: 30% - Video background music, commercial licensing
Endel
Risk: 25% - Adaptive soundscapes, wellness focus, low commercial risk
Human Artists
Risk: 5% - Naturally created music, standard copyright protections
How AI Detection Works
Step 1: Metadata Enrichment
System fetches track information from multiple sources:
- Spotify: Release date, label, popularity, artist followers
- YouTube: Video views, likes, comments, engagement metrics
- Deezer: Track rank, availability, audio preview URL
Importance: Metadata provides context clues about track authenticity
Step 2: Audio Download & Analysis
If audio preview available (Deezer):
- Download 30-second preview (15-second timeout)
- Extract audio features with librosa library
- Analyze spectral, temporal, and timbral characteristics
- Compare against AI detection indicators
Note: If no audio available, system continues with metadata-only analysis
Step 3: Multi-AI Platform Analysis
System queries three AI platforms simultaneously:
- OpenAI GPT-4: Pattern recognition and text analysis
- Anthropic Claude Opus 4.1: Deep reasoning and context understanding
- Perplexity Sonar: Web search and metadata validation
Each AI analyzes track information and provides platform likelihood scores
Step 4: Aggregate Platform Detection
System combines results from all three AIs:
- Parse structured data from each AI response
- Calculate weighted average for each platform
- Determine maximum confidence platform
- Validate consistency across AI responses
Priority: Structured data > Text parsing for accuracy
Step 5: Confidence-Scaled Risk Assessment
Risk scores calculated using formula:
Risk = Platform Base Risk × (Confidence / 100)
Then adjusted for metadata factors:
- Major labels: -30 copyright, -25 legal
- Pre-2020 releases: -50 all risks (pre-AI era)
- High followers (>100K): -20 ownership, -15 market
AI Detection Audio Indicators
Specific audio characteristics that suggest AI generation:
Low Spectral Variance
AI music has more uniform frequency distribution (<300 variance)
Compressed Dynamics
AI tracks show limited dynamic range (<0.05 range)
Low Spectral Flatness
AI music is more tonal, less noisy (<0.05 flatness)
MFCC Uniformity
AI shows consistent timbral texture (std <10)
Human Music Characteristics:
- Higher spectral variance (natural performance variations)
- Greater dynamic range (expressive performance)
- More spectral flatness (acoustic instruments)
- Variable MFCCs (natural timbral changes)
Accuracy & Limitations
Current Accuracy (Beta)
- Human Detection: Target 85-95% (validation in progress)
- AI Platform ID: 70-85% for Suno/Udio
- Risk Assessment: 80-90% correlation with expert reviews
Note: Beta testing will refine these accuracy metrics
Known Limitations
- Not all tracks have audio previews (metadata-only less accurate)
- Human-AI collaborations may produce ambiguous results
- Heavily post-processed AI music may appear more human-like
- New AI platforms not yet in training data may be missed
Real-World Use Cases
Independent Artist Protection
Scenario: Artist suspects their song was copied by AI platform
- Verify original track (expect Human 85-95%)
- Verify suspected AI copy (expect Suno/Udio 70-85%)
- Compare risk scores and audio features
- Generate evidence report for legal action
Record Label Due Diligence
Scenario: Label evaluating signing new artist
- Verify all tracks in artist's catalog
- Check for AI generation patterns
- Review risk scores for licensing viability
- Make informed signing decision
Playlist Curator Verification
Scenario: Curator maintaining organic playlist
- Verify all tracks before adding to playlist
- Ensure Human likelihood >80%
- Maintain playlist integrity
- Avoid AI-generated content