Here are the 30,000 songs Sony is suing Udio’s AI music generator over

Introduction

In the wake of ongoing copyright disputes involving AI music generators like Udio, understanding how to work with AI music generation tools is becoming increasingly important for creators and developers. This tutorial will guide you through creating a practical music analysis tool that can help identify potential copyright issues in AI-generated music by examining audio features and comparing them to known datasets. While this doesn't replicate the complex legal analysis involved in lawsuits like Sony's against Udio, it demonstrates the technical foundations of how such systems might work.

Prerequisites

• Python 3.8 or higher installed
• Basic understanding of audio processing concepts
• Intermediate knowledge of machine learning concepts
• Required Python packages: librosa, numpy, scikit-learn, pandas

Step-by-Step Instructions

1. Setting Up Your Environment

1.1 Install Required Dependencies

First, we need to install the necessary Python libraries for audio processing and machine learning:

pip install librosa numpy scikit-learn pandas

Why: These libraries provide the core functionality for audio analysis (librosa), numerical operations (numpy), machine learning (scikit-learn), and data manipulation (pandas).

1.2 Create Project Structure

Create a new directory for this project and set up the following structure:

music_analysis_project/
├── audio_samples/
├── data/
├── models/
├── analysis.py
└── main.py

Why: Organizing your code into logical directories makes it easier to maintain and scale as your project grows.

2. Audio Feature Extraction

2.1 Implement Audio Analysis Functions

Create the core audio analysis functionality in analysis.py:

import librosa
import numpy as np
import pandas as pd

def extract_audio_features(audio_file_path):
    """Extract key audio features from a music file"""
    # Load audio file
    y, sr = librosa.load(audio_file_path)
    
    # Extract features
    tempo, _ = librosa.beat.beat_track(y=y, sr=sr)
    
    # Spectral features
    spectral_centroids = librosa.feature.spectral_centroid(y=y, sr=sr).mean()
    spectral_rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr).mean()
    spectral_bandwidth = librosa.feature.spectral_bandwidth(y=y, sr=sr).mean()
    
    # MFCCs (Mel-frequency cepstral coefficients)
    mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
    mfccs_mean = mfccs.mean(axis=1)
    
    # Zero crossing rate
    zero_crossing_rate = librosa.feature.zero_crossing_rate(y).mean()
    
    # Chroma features
    chroma = librosa.feature.chroma_stft(y=y, sr=sr).mean(axis=1)
    
    # Create feature dictionary
    features = {
        'tempo': tempo,
        'spectral_centroid': spectral_centroids,
        'spectral_rolloff': spectral_rolloff,
        'spectral_bandwidth': spectral_bandwidth,
        'zero_crossing_rate': zero_crossing_rate,
        'mfcc1': mfccs_mean[0],
        'mfcc2': mfccs_mean[1],
        'mfcc3': mfccs_mean[2],
        'mfcc4': mfccs_mean[3],
        'mfcc5': mfccs_mean[4],
        'chroma1': chroma[0],
        'chroma2': chroma[1],
        'chroma3': chroma[2],
        'chroma4': chroma[3],
        'chroma5': chroma[4],
        'chroma6': chroma[5],
        'chroma7': chroma[6],
        'chroma8': chroma[7],
        'chroma9': chroma[8],
        'chroma10': chroma[9],
        'chroma11': chroma[10],
        'chroma12': chroma[11]
    }
    
    return features

Why: These features help characterize the audio signature of a track, which can be used to identify similarities between different pieces of music.

2.2 Create Feature Comparison Function

Add this function to your analysis.py file:

def compare_audio_features(features1, features2, threshold=0.8):
    """Compare two sets of audio features and return similarity score"""
    # Convert to numpy arrays
    arr1 = np.array(list(features1.values()))
    arr2 = np.array(list(features2.values()))
    
    # Calculate cosine similarity
    dot_product = np.dot(arr1, arr2)
    norm1 = np.linalg.norm(arr1)
    norm2 = np.linalg.norm(arr2)
    
    if norm1 == 0 or norm2 == 0:
        return 0
    
    similarity = dot_product / (norm1 * norm2)
    
    # Return binary comparison based on threshold
    return similarity >= threshold

Why: This function allows us to quantitatively compare audio signatures, which is crucial for identifying potential copyright issues.

3. Building the Music Analysis System

3.1 Create Main Analysis Script

Implement the main analysis logic in main.py:

import os
import json
from analysis import extract_audio_features, compare_audio_features

def load_reference_dataset(dataset_path):
    """Load reference audio features from a dataset"""
    reference_features = {}
    
    # In a real scenario, this would load from a database or CSV
    # For this example, we'll simulate with sample data
    
    # This would typically load pre-computed features from known copyrighted works
    # For demonstration, we'll create a simple mock dataset
    
    return reference_features

def analyze_new_track(audio_file_path, reference_dataset):
    """Analyze a new track against reference dataset"""
    # Extract features from the new track
    new_features = extract_audio_features(audio_file_path)
    
    # Compare against reference dataset
    matches = []
    
    for track_id, reference_features in reference_dataset.items():
        similarity = compare_audio_features(new_features, reference_features)
        if similarity:
            matches.append((track_id, similarity))
    
    return matches

# Example usage
if __name__ == "__main__":
    # Load reference dataset (in real implementation, this would load from file)
    reference_dataset = load_reference_dataset('data/reference_dataset.json')
    
    # Analyze a new track
    new_track_path = 'audio_samples/new_track.mp3'
    
    if os.path.exists(new_track_path):
        matches = analyze_new_track(new_track_path, reference_dataset)
        print(f"Found {len(matches)} potential matches")
        for track_id, similarity in matches:
            print(f"Track {track_id}: {similarity:.2f} similarity")
    else:
        print("Audio file not found")

Why: This structure mimics how a copyright analysis system would work, comparing new AI-generated music against a database of known copyrighted works.

3.2 Generate Mock Reference Dataset

Create a simple reference dataset for demonstration:

# In data/reference_dataset.json
{
  "elvis_hound_dog": {
    "tempo": 120.0,
    "spectral_centroid": 3500.0,
    "spectral_rolloff": 5000.0,
    "spectral_bandwidth": 2000.0,
    "zero_crossing_rate": 0.05,
    "mfcc1": 0.1,
    "mfcc2": 0.2,
    "mfcc3": 0.3,
    "mfcc4": 0.4,
    "mfcc5": 0.5,
    "chroma1": 0.1,
    "chroma2": 0.2,
    "chroma3": 0.3,
    "chroma4": 0.4,
    "chroma5": 0.5,
    "chroma6": 0.6,
    "chroma7": 0.7,
    "chroma8": 0.8,
    "chroma9": 0.9,
    "chroma10": 1.0,
    "chroma11": 0.9,
    "chroma12": 0.8
  },
  "beyonce_say_my_name": {
    "tempo": 110.0,
    "spectral_centroid": 4200.0,
    "spectral_rolloff": 6000.0,
    "spectral_bandwidth": 2500.0,
    "zero_crossing_rate": 0.08,
    "mfcc1": 0.2,
    "mfcc2": 0.3,
    "mfcc3": 0.4,
    "mfcc4": 0.5,
    "mfcc5": 0.6,
    "chroma1": 0.2,
    "chroma2": 0.3,
    "chroma3": 0.4,
    "chroma4": 0.5,
    "chroma5": 0.6,
    "chroma6": 0.7,
    "chroma7": 0.8,
    "chroma8": 0.9,
    "chroma9": 1.0,
    "chroma10": 0.9,
    "chroma11": 0.8,
    "chroma12": 0.7
  }
}

Why: This mock dataset represents how a real copyright analysis system would store reference audio signatures for comparison.

4. Running the Analysis

4.1 Test Your Implementation

Run your analysis system:

python main.py

Why: This executes your complete analysis pipeline, demonstrating how audio features are extracted and compared.

4.2 Extend with Real Audio Files

Replace the mock dataset with actual audio features extracted from real copyrighted songs:

# To extract features from real audio files:
# 1. Add this function to analysis.py

def extract_and_save_features(audio_files, output_file):
    """Extract features from multiple audio files and save to JSON"""
    all_features = {}
    
    for audio_file in audio_files:
        try:
            features = extract_audio_features(audio_file)
            all_features[audio_file] = features
        except Exception as e:
            print(f"Error processing {audio_file}: {e}")
    
    with open(output_file, 'w') as f:
        json.dump(all_features, f, indent=2)
    
    print(f"Saved {len(all_features)} features to {output_file}")

Why: This allows you to build a comprehensive reference database that can be used for actual copyright analysis.

Summary

This tutorial demonstrated how to build a foundational music analysis system that could potentially be used in copyright infringement detection. By extracting audio features like tempo, spectral characteristics, MFCCs, and chroma features, we created a system that can compare new audio tracks against a reference database. While this is a simplified implementation, it illustrates the core technical concepts behind systems that companies like Sony might use to identify potential copyright issues in AI-generated music. In practice, such systems would require much more sophisticated feature engineering, larger databases, and legal expertise to make definitive copyright determinations.