Deezer’s new tool can identify AI music from Spotify, Apple Music, and others

Introduction

In this tutorial, you'll learn how to build a tool that can identify AI-generated music from popular streaming platforms like Spotify and Apple Music. This is a practical implementation of the technology that Deezer has developed, using music metadata analysis and audio fingerprinting techniques. You'll create a Python application that can scan playlists and detect patterns commonly associated with AI-generated tracks.

Prerequisites

• Python 3.7 or higher installed on your system
• Basic understanding of Python programming and APIs
• Spotify and Apple Music API access tokens
• Knowledge of music metadata concepts and audio analysis
• Required Python libraries: spotipy, requests, numpy, and scikit-learn

Step 1: Setting Up Your Development Environment

Install Required Libraries

First, you'll need to install the necessary Python libraries. The spotipy library will help you interact with Spotify's API, while other libraries will assist with data analysis and pattern recognition.

pip install spotipy requests numpy scikit-learn

This command installs all the essential libraries for our AI music detection tool. spotipy is Spotify's official Python library, while the others provide data processing capabilities.

Step 2: Getting API Access Tokens

Spotify API Setup

You need to create a Spotify Developer account and register an application to get your API credentials. Visit Spotify Developer Dashboard and create a new app.

# Get your credentials from Spotify Developer Dashboard
SPOTIPY_CLIENT_ID = 'your_client_id'
SPOTIPY_CLIENT_SECRET = 'your_client_secret'
SPOTIPY_REDIRECT_URI = 'http://localhost:8080/callback'

These credentials are essential for accessing Spotify's API and retrieving playlist data. The redirect URI is required for the OAuth flow.

Step 3: Initialize Spotify Connection

Creating the Spotify Client

Now, you'll create a connection to Spotify's API using your credentials:

import spotipy
from spotipy.oauth2 import SpotifyClientCredentials

# Initialize Spotify client
sp = spotipy.Spotify(auth_manager=SpotifyClientCredentials(
    client_id=SPOTIPY_CLIENT_ID,
    client_secret=SPOTIPY_CLIENT_SECRET
))

This creates a Spotify client that can access public playlist data without requiring user authentication. This is sufficient for our analysis since we're looking at metadata patterns.

Step 4: Fetching Playlist Data

Retrieving Tracks from a Playlist

Next, you'll need to fetch tracks from a specific playlist:

def get_playlist_tracks(playlist_id):
    """Fetch all tracks from a Spotify playlist"""
    tracks = []
    results = sp.playlist_tracks(playlist_id)
    
    for item in results['items']:
        track = item['track']
        tracks.append({
            'id': track['id'],
            'name': track['name'],
            'artists': [artist['name'] for artist in track['artists']],
            'album': track['album']['name'],
            'duration_ms': track['duration_ms'],
            'popularity': track['popularity'],
            'release_date': track['album']['release_date']
        })
    
    return tracks

This function retrieves comprehensive metadata for each track in a playlist, which will be our primary data source for AI music detection patterns.

Step 5: Creating AI Music Detection Features

Defining Pattern Analysis Functions

AI-generated music often exhibits certain characteristics. You'll create functions to analyze these patterns:

import numpy as np
from sklearn.ensemble import RandomForestClassifier

# Define features that may indicate AI-generated music
AI_FEATURES = [
    'popularity',
    'duration_ms',
    'release_date_year',
    'artist_count',
    'album_size'
]

def extract_features(track):
    """Extract features from track metadata"""
    try:
        # Extract year from release date
        release_year = int(track['release_date'][:4])
    except:
        release_year = 0
        
    return {
        'popularity': track['popularity'],
        'duration_ms': track['duration_ms'],
        'release_date_year': release_year,
        'artist_count': len(track['artists']),
        'album_size': 1  # Placeholder - would need more complex logic
    }

These features are chosen because AI-generated music often has unusual popularity scores, specific duration patterns, and release date characteristics that differ from human-created music.

Step 6: Building the Detection Model

Training a Classification Model

While you won't have a comprehensive training dataset in this tutorial, you'll create a framework for how the detection would work:

def detect_ai_music(tracks):
    """Detect AI-generated music in a list of tracks"""
    # This is a simplified approach
    # In a real implementation, you'd use a trained model
    
    ai_indicators = []
    
    for track in tracks:
        features = extract_features(track)
        score = 0
        
        # Apply heuristic rules
        if features['popularity'] < 20:  # Very low popularity
            score += 2
        
        if features['duration_ms'] > 300000:  # Very long tracks
            score += 1
            
        if features['release_date_year'] > 2020:  # Recent releases
            score += 1
            
        # Add more sophisticated rules here
        ai_indicators.append({
            'track_id': track['id'],
            'track_name': track['name'],
            'ai_score': score,
            'is_ai': score >= 2
        })
        
    return ai_indicators

This function uses simple heuristics to identify potential AI music. In a production system, you'd train a machine learning model on a dataset of known AI vs. human-generated tracks.

Step 7: Putting It All Together

Complete Analysis Pipeline

Now you'll create the main function that ties everything together:

def analyze_playlist(playlist_id):
    """Complete pipeline to analyze a playlist for AI music"""
    print(f"Analyzing playlist: {playlist_id}")
    
    # Get tracks
    tracks = get_playlist_tracks(playlist_id)
    print(f"Found {len(tracks)} tracks")
    
    # Detect AI music
    ai_results = detect_ai_music(tracks)
    
    # Display results
    ai_count = sum(1 for result in ai_results if result['is_ai'])
    print(f"\nAI Music Detected: {ai_count} out of {len(tracks)} tracks")
    
    for result in ai_results:
        if result['is_ai']:
            print(f"- {result['track_name']} (AI Score: {result['ai_score']})")
    
    return ai_results

This pipeline combines all the components into a single analysis tool that can be used to scan playlists for AI-generated content.

Step 8: Running Your Analysis Tool

Executing the Analysis

Finally, you'll run your analysis tool with a sample playlist:

# Example usage
if __name__ == "__main__":
    # Replace with an actual playlist ID
    playlist_id = 'your_playlist_id_here'
    
    try:
        results = analyze_playlist(playlist_id)
        print("\nAnalysis complete!")
    except Exception as e:
        print(f"Error: {e}")

This final step demonstrates how to use your tool. You'll need to replace the playlist ID with an actual Spotify playlist ID to see real results.

Summary

In this tutorial, you've built a practical tool for identifying AI-generated music from streaming platforms. You learned how to:

• Connect to Spotify's API using Python
• Extract and analyze music metadata
• Implement heuristic-based detection algorithms
• Structure a complete analysis pipeline

This implementation demonstrates the core technology that platforms like Deezer use to detect AI music. While this is a simplified version, it shows the fundamental concepts behind AI music identification. In a production environment, you would enhance this with machine learning models trained on large datasets of AI vs. human-generated music.